Collections & Models ==================== ``ATOMMIC`` is organized in collections, each of which implements a specific task. The following collections are currently available, implementing various models as listed. MultiTask Learning (MTL) ------------------------ End-to-End Recurrent Attention Network ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End-to-End Recurrent Attention Network (:class:`~atommic.collections.multitask.rs.nn.seranet.SERANet`), as presented in [Huang2019]_. References ---------- .. [Huang2019] Huang, Q., Chen, X., Metaxas, D., Nadar, M.S. (2019). Brain Segmentation from k-Space with End-to-End Recurrent Attention Network. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_31 Example configuration: .. code-block:: bash model: model_name: SERANET use_reconstruction_module: true input_channels: 2 reconstruction_module: unet reconstruction_module_output_channels: 2 reconstruction_module_channels: 32 reconstruction_module_pooling_layers: 4 reconstruction_module_dropout: 0.0 # or # reconstruction_module: cascadenet # reconstruction_module_hidden_channels: 32 # reconstruction_module_n_convs: 2 # reconstruction_module_batchnorm: true # reconstruction_module_num_cascades: 5 reconstruction_module_num_blocks: 3 segmentation_module_input_channels: 32 segmentation_module_output_channels: 2 segmentation_module_channels: 32 segmentation_module_pooling_layers: 4 segmentation_module_dropout: 0.0 recurrent_module_iterations: 2 recurrent_module_attention_channels: 32 recurrent_module_attention_pooling_layers: 4 recurrent_module_attention_dropout: 0.0 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs complex_data: true consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false Image domain Deep Structured Low-Rank Network ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Image domain Deep Structured Low-Rank Network (:class:`~atommic.collections.multitask.rs.nn.idslr.IDSLR`), as presented in [Pramanik2021]_. References ---------- .. [Pramanik2021] Pramanik A, Wu X, Jacob M. Joint calibrationless reconstruction and segmentation of parallel MRI. arXiv preprint arXiv:2105.09220. 2021 May 19. Example configuration: .. code-block:: bash model: model_name: IDSLR use_reconstruction_module: true input_channels: 24 # coils * 2 reconstruction_module_output_channels: 24 # coils * 2 segmentation_module_output_channels: 2 channels: 64 num_pools: 2 padding_size: 11 drop_prob: 0.0 normalize: true padding: true norm_groups: 2 num_iters: 5 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs complex_data: true consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false Image domain Deep Structured Low-Rank UNet ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Image domain Deep Structured Low-Rank network using a UNet (and not only the decoder part) as segmentation model (:class:`~atommic.collections.multitask.rs.nn.idslr_unet.IDSLRUNet`), as presented in [Pramanik2021]_. References ---------- .. [Pramanik2021] Pramanik A, Wu X, Jacob M. Joint calibrationless reconstruction and segmentation of parallel MRI. arXiv preprint arXiv:2105.09220. 2021 May 19. Example configuration: .. code-block:: bash model: model_name: IDSLRUNET use_reconstruction_module: true input_channels: 24 # coils * 2 reconstruction_module_output_channels: 24 # coils * 2 segmentation_module_output_channels: 2 channels: 64 num_pools: 2 padding_size: 11 drop_prob: 0.0 normalize: true padding: true norm_groups: 2 num_iters: 5 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs complex_data: true consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false Multi-Task Learning for MRI Reconstruction and Segmentation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Multi-Task Learning for MRI Reconstruction and Segmentation (:class:`~atommic.collections.multitask.rs.nn.mtlrs.MTLRS`), as presented in [Karkalousos2023]_. References ---------- .. [Karkalousos2023] Karkalousos, D., Išgum, I., Marquering, H., Caan, M. W. A., (2023). MultiTask Learning for accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis. In Proceedings of Machine Learning Research (Vol. 078). Example configuration: .. code-block:: bash model: model_name: MTLRS joint_reconstruction_segmentation_module_cascades: 5 task_adaption_type: multi_task_learning use_reconstruction_module: true reconstruction_module_recurrent_layer: IndRNN reconstruction_module_conv_filters: - 64 - 64 - 2 reconstruction_module_conv_kernels: - 5 - 3 - 3 reconstruction_module_conv_dilations: - 1 - 2 - 1 reconstruction_module_conv_bias: - true - true - false reconstruction_module_recurrent_filters: - 64 - 64 - 0 reconstruction_module_recurrent_kernels: - 1 - 1 - 0 reconstruction_module_recurrent_dilations: - 1 - 1 - 0 reconstruction_module_recurrent_bias: - true - true - false reconstruction_module_depth: 2 reconstruction_module_time_steps: 8 reconstruction_module_conv_dim: 2 reconstruction_module_num_cascades: 1 reconstruction_module_dimensionality: 2 reconstruction_module_no_dc: true reconstruction_module_keep_prediction: true reconstruction_module_accumulate_predictions: true segmentation_module: AttentionUNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 2 segmentation_module_channels: 64 segmentation_module_pooling_layers: 2 segmentation_module_dropout: 0.0 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs complex_data: true consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false Reconstruction Segmentation method using UNet ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Reconstruction Segmentation method using UNets for both the reconstruction and segmentation (:class:`~atommic.collections.multitask.rs.nn.recseg_unet.RecSegUNet`), as presented in [Sui2021]_. References ---------- .. [Sui2021] Sui, B, Lv, J, Tong, X, Li, Y, Wang, C. Simultaneous image reconstruction and lesion segmentation in accelerated MRI using multitasking learning. Med Phys. 2021; 48: 7189– 7198. https://doi.org/10.1002/mp.15213 Example configuration: .. code-block:: bash model: model_name: RECSEGNET use_reconstruction_module: true input_channels: 1 reconstruction_module_output_channels: 1 reconstruction_module_channels: 64 reconstruction_module_pooling_layers: 2 reconstruction_module_dropout: 0.0 segmentation_module_output_channels: 2 segmentation_module_channels: 64 segmentation_module_pooling_layers: 2 segmentation_module_dropout: 0.0 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs complex_data: true consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false Segmentation Network MRI ~~~~~~~~~~~~~~~~~~~~~~~~ Segmentation Network MRI (:class:`~atommic.collections.multitask.rs.nn.segnet.SegNet`), as presented in [Sun2019]_. References ---------- .. [Sun2019] Sun, L., Fan, Z., Ding, X., Huang, Y., Paisley, J. (2019). Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_38 Example configuration: .. code-block:: bash model: model_name: SEGNET use_reconstruction_module: true input_channels: 24 # coils * 2 reconstruction_module_output_channels: 24 # coils * 2 segmentation_module_output_channels: 2 channels: 64 num_pools: 2 padding_size: 11 drop_prob: 0.0 normalize: true padding: true norm_groups: 2 num_cascades: 5 segmentation_final_layer_conv_dim: 2 segmentation_final_layer_kernel_size: 3 segmentation_final_layer_dilation: 1 segmentation_final_layer_bias: False segmentation_final_layer_nonlinear: relu # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs complex_data: true consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false Quantitative MR Imaging (qMRI) ------------------------------ quantitative Cascades of Independently Recurrent Inference Machines ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ quantitative Cascades of Independently Recurrent Inference Machines (:class:`~atommic.collections.quantitative.nn.qcirim.qCIRIM`), as presented in [Zhang2022]_. References ---------- .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW. A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine. NeuroImage. 2022 Dec 1;264:119680. Example configuration: .. code-block:: bash model: model_name: qCIRIM use_reconstruction_module: true reconstruction_module_recurrent_layer: IndRNN reconstruction_module_conv_filters: - 64 - 64 - 2 reconstruction_module_conv_kernels: - 5 - 3 - 3 reconstruction_module_conv_dilations: - 1 - 2 - 1 reconstruction_module_conv_bias: - true - true - false reconstruction_module_recurrent_filters: - 64 - 64 - 0 reconstruction_module_recurrent_kernels: - 1 - 1 - 0 reconstruction_module_recurrent_dilations: - 1 - 1 - 0 reconstruction_module_recurrent_bias: - true - true - false reconstruction_module_depth: 2 reconstruction_module_time_steps: 8 reconstruction_module_conv_dim: 2 reconstruction_module_num_cascades: 1 reconstruction_module_dimensionality: 2 reconstruction_module_no_dc: true reconstruction_module_keep_prediction: true reconstruction_module_accumulate_predictions: true quantitative_module_recurrent_layer: IndRNN quantitative_module_conv_filters: - 64 - 64 - 4 quantitative_module_conv_kernels: - 5 - 3 - 3 quantitative_module_conv_dilations: - 1 - 2 - 1 quantitative_module_conv_bias: - true - true - false quantitative_module_recurrent_filters: - 64 - 64 - 0 quantitative_module_recurrent_kernels: - 1 - 1 - 0 quantitative_module_recurrent_dilations: - 1 - 1 - 0 quantitative_module_recurrent_bias: - true - true - false quantitative_module_depth: 2 quantitative_module_time_steps: 8 quantitative_module_conv_dim: 2 quantitative_module_num_cascades: 1 quantitative_module_no_dc: true quantitative_module_keep_prediction: true quantitative_module_accumulate_predictions: true quantitative_module_signal_forward_model_sequence: MEGRE quantitative_module_dimensionality: 2 quantitative_module_gamma_regularization_factors: - 150.0 - 150.0 - 1000.0 - 150.0 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 shift_B0_input: false normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false quantitative Recurrent Inference Machines ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ quantitative Recurrent Inference Machines (:class:`~atommic.collections.quantitative.nn.qrim_base.qrim_block.qRIMBlock`), as presented in [Zhang2022]_. References ---------- .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW. A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine. NeuroImage. 2022 Dec 1;264:119680. Example configuration: .. code-block:: bash model: model_name: qCIRIM use_reconstruction_module: true reconstruction_module_recurrent_layer: GRU reconstruction_module_conv_filters: - 64 - 64 - 2 reconstruction_module_conv_kernels: - 5 - 3 - 3 reconstruction_module_conv_dilations: - 1 - 2 - 1 reconstruction_module_conv_bias: - true - true - false reconstruction_module_recurrent_filters: - 64 - 64 - 0 reconstruction_module_recurrent_kernels: - 1 - 1 - 0 reconstruction_module_recurrent_dilations: - 1 - 1 - 0 reconstruction_module_recurrent_bias: - true - true - false reconstruction_module_depth: 2 reconstruction_module_time_steps: 8 reconstruction_module_conv_dim: 2 reconstruction_module_num_cascades: 1 reconstruction_module_dimensionality: 2 reconstruction_module_no_dc: true reconstruction_module_keep_prediction: true reconstruction_module_accumulate_predictions: true quantitative_module_recurrent_layer: GRU quantitative_module_conv_filters: - 64 - 64 - 4 quantitative_module_conv_kernels: - 5 - 3 - 3 quantitative_module_conv_dilations: - 1 - 2 - 1 quantitative_module_conv_bias: - true - true - false quantitative_module_recurrent_filters: - 64 - 64 - 0 quantitative_module_recurrent_kernels: - 1 - 1 - 0 quantitative_module_recurrent_dilations: - 1 - 1 - 0 quantitative_module_recurrent_bias: - true - true - false quantitative_module_depth: 2 quantitative_module_time_steps: 8 quantitative_module_conv_dim: 2 quantitative_module_num_cascades: 1 quantitative_module_no_dc: true quantitative_module_keep_prediction: true quantitative_module_accumulate_predictions: true quantitative_module_signal_forward_model_sequence: MEGRE quantitative_module_dimensionality: 2 quantitative_module_gamma_regularization_factors: - 150.0 - 150.0 - 1000.0 - 150.0 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 shift_B0_input: false normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false quantitative End-to-End Variational Network ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ quantitative End-to-End Variational Network (:class:`~atommic.collections.quantitative.nn.qvarnet.qVarNet`), as presented in [Zhang2022]_. References ---------- .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW. A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine. NeuroImage. 2022 Dec 1;264:119680. Example configuration: .. code-block:: bash model: model_name: qVN use_reconstruction_module: false reconstruction_module_num_cascades: 2 reconstruction_module_channels: 8 reconstruction_module_pooling_layers: 2 reconstruction_module_in_channels: 2 reconstruction_module_out_channels: 2 reconstruction_module_padding_size: 11 reconstruction_module_normalize: true reconstruction_module_no_dc: false reconstruction_module_accumulate_predictions: false quantitative_module_num_cascades: 1 quantitative_module_channels: 4 quantitative_module_pooling_layers: 2 quantitative_module_in_channels: 8 quantitative_module_out_channels: 8 quantitative_module_padding_size: 11 quantitative_module_normalize: true quantitative_module_no_dc: false quantitative_module_dimensionality: 2 quantitative_module_accumulate_predictions: false quantitative_module_signal_forward_model_sequence: MEGRE quantitative_module_gamma_regularization_factors: - 150.0 - 150.0 - 1000.0 - 150.0 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 shift_B0_input: false normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false MRI Reconstruction (REC) ------------------------ Cascades of Independently Recurrent Inference Machines ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Cascades of Independently Recurrent Inference Machines (:class:`~atommic.collections.reconstruction.nn.cirim.CIRIM`), as presented in [Karkalousos2022]_. References ---------- .. [Karkalousos2022] Karkalousos D, Noteboom S, Hulst HE, Vos FM, Caan MWA. Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction. Phys Med Biol. 2022 Jun 8;67(12). doi: 10.1088/1361-6560/ac6cc2. PMID: 35508147. Example configuration: .. code-block:: bash model: model_name: CIRIM recurrent_layer: IndRNN conv_filters: - 64 - 64 - 2 conv_kernels: - 5 - 3 - 3 conv_dilations: - 1 - 2 - 1 conv_bias: - true - true - false recurrent_filters: - 64 - 64 - 0 recurrent_kernels: - 1 - 1 - 0 recurrent_dilations: - 1 - 1 - 0 recurrent_bias: - true - true - false depth: 2 time_steps: 8 conv_dim: 2 num_cascades: 8 no_dc: true keep_prediction: true accumulate_predictions: true # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Convolutional Recurrent Neural Networks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Convolutional Recurrent Neural Networks (:class:`~atommic.collections.reconstruction.nn.crnn.CRNNet`), as presented in [Qin2019]_. References ---------- .. [Qin2019] C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal and D. Rueckert, "Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol. 38, no. 1, pp. 280-290, Jan. 2019, doi: 10.1109/TMI.2018.2863670. Example configuration: .. code-block:: bash model: model_name: CRNNet num_iterations: 10 hidden_channels: 64 n_convs: 3 batchnorm: false no_dc: false accumulate_predictions: true # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Deep Cascade of Convolutional Neural Networks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Deep Cascade of Convolutional Neural Networks (:class:`~atommic.collections.reconstruction.nn.ccnn.CascadeNet`), as presented in [Schlemper2017]_. References ---------- .. [Schlemper2017] Schlemper, J., Caballero, J., Hajnal, J. V., Price, A., & Rueckert, D., A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction. Information Processing in Medical Imaging (IPMI), 2017. Example configuration: .. code-block:: bash model: model_name: CascadeNet num_cascades: 10 hidden_channels: 64 n_convs: 5 batchnorm: false no_dc: false accumulate_predictions: false # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Down-Up Net ~~~~~~~~~~~ Down-Up NET (:class:`~atommic.collections.reconstruction.nn.dunet.DUNet`), inspired by [Hammernik2021]_. References ---------- .. [Hammernik2021] Hammernik, K, Schlemper, J, Qin, C, et al. Systematic valuation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination. Magn Reson Med. 2021; 86: 1859– 1872. https://doi.org/10.1002/mrm.28827 Example configuration: .. code-block:: bash model: model_name: DUNet num_iter: 10 reg_model_architecture: DIDN didn_hidden_channels: 64 didn_num_dubs: 2 didn_num_convs_recon: 1 data_consistency_term: VS data_consistency_lambda_init: 0.1 data_consistency_iterations: 10 shared_params: false # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false End-to-End Variational Network ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End-to-End Variational Network (:class:`~atommic.collections.reconstruction.nn.varnet.VarNet`), as presented in [Sriram2020]_. References ---------- .. [Sriram2020] Sriram A, Zbontar J, Murrell T, Defazio A, Zitnick CL, Yakubova N, Knoll F, Johnson P. End-to-end variational networks for accelerated MRI reconstruction. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2020 Oct 4 (pp. 64-73). Springer, Cham. Example configuration: .. code-block:: bash model: model_name: VN num_cascades: 8 channels: 18 pooling_layers: 4 padding_size: 11 normalize: true no_dc: false # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Independently Recurrent Inference Machines ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Independently Recurrent Inference Machines (:class:`~atommic.collections.reconstruction.nn.rim_base.rim_block.RIMBlock`), as presented in [Karkalousos2022]_. References ---------- .. [Karkalousos2022] Karkalousos D, Noteboom S, Hulst HE, Vos FM, Caan MWA. Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction. Phys Med Biol. 2022 Jun 8;67(12). doi: 10.1088/1361-6560/ac6cc2. PMID: 35508147. Example configuration: .. code-block:: bash model: model_name: CIRIM recurrent_layer: IndRNN conv_filters: - 64 - 64 - 2 conv_kernels: - 5 - 3 - 3 conv_dilations: - 1 - 2 - 1 conv_bias: - true - true - false recurrent_filters: - 64 - 64 - 0 recurrent_kernels: - 1 - 1 - 0 recurrent_dilations: - 1 - 1 - 0 recurrent_bias: - true - true - false depth: 2 time_steps: 8 conv_dim: 2 num_cascades: 1 no_dc: true keep_prediction: true accumulate_predictions: true # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (:class:`~atommic.collections.reconstruction.nn.jointicnet.JointICNet`), as presented in [Jun2021]_. References ---------- .. [Jun2021] Jun, Yohan, et al. “Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI.” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2021, pp. 5266–75. DOI.org (Crossref), https://doi.org/10.1109/CVPR46437.2021.00523. Example configuration: .. code-block:: bash model: model_name: JointICNet num_iter: 2 kspace_unet_num_filters: 16 kspace_unet_num_pool_layers: 2 kspace_unet_dropout_probability: 0.0 kspace_unet_padding_size: 11 kspace_unet_normalize: true imspace_unet_num_filters: 16 imspace_unet_num_pool_layers: 2 imspace_unet_dropout_probability: 0.0 imspace_unet_padding_size: 11 imspace_unet_normalize: true sens_unet_num_filters: 16 sens_unet_num_pool_layers: 2 sens_unet_dropout_probability: 0.0 sens_unet_padding_size: 11 sens_unet_normalize: true # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false KIKINet ~~~~~~~ KIKINet (:class:`~atommic.collections.reconstruction.nn.kikinet.KIKINet`), modified to work with multi-coil k-space data, as presented in [Taejoon2018]_. References ---------- .. [Taejoon2018] Eo, Taejoon, et al. “KIKI-Net: Cross-Domain Convolutional Neural Networks for Reconstructing Undersampled Magnetic Resonance Images.” Magnetic Resonance in Medicine, vol. 80, no. 5, Nov. 2018, pp. 2188–201. PubMed, https://doi.org/10.1002/mrm.27201. Example configuration: .. code-block:: bash model: model_name: KIKINet num_iter: 2 kspace_model_architecture: UNET kspace_in_channels: 2 kspace_out_channels: 2 kspace_unet_num_filters: 16 kspace_unet_num_pool_layers: 2 kspace_unet_dropout_probability: 0.0 kspace_unet_padding_size: 11 kspace_unet_normalize: true imspace_model_architecture: UNET imspace_in_channels: 2 imspace_unet_num_filters: 16 imspace_unet_num_pool_layers: 2 imspace_unet_dropout_probability: 0.0 imspace_unet_padding_size: 11 imspace_unet_normalize: true # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Learned Primal-Dual Net ~~~~~~~~~~~~~~~~~~~~~~~ Learned Primal-Dual Net (:class:`~atommic.collections.reconstruction.nn.lpd.LPDNet`), as presented in [Adler2018]_. References ---------- .. [Adler2018] Adler, Jonas, and Ozan Öktem. “Learned Primal-Dual Reconstruction.” IEEE Transactions on Medical Imaging, vol. 37, no. 6, June 2018, pp. 1322–32. arXiv.org, https://doi.org/10.1109/TMI.2018.2799231. Example configuration: .. code-block:: bash model: model_name: LPDNet num_primal: 5 num_dual: 5 num_iter: 5 primal_model_architecture: UNET primal_in_channels: 2 primal_out_channels: 2 primal_unet_num_filters: 16 primal_unet_num_pool_layers: 2 primal_unet_dropout_probability: 0.0 primal_unet_padding_size: 11 primal_unet_normalize: true dual_model_architecture: UNET dual_in_channels: 2 dual_out_channels: 2 dual_unet_num_filters: 16 dual_unet_num_pool_layers: 2 dual_unet_dropout_probability: 0.0 dual_unet_padding_size: 11 dual_unet_normalize: true # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false MoDL: Model Based Deep Learning Architecture for Inverse Problems ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MoDL: Model Based Deep Learning Architecture for Inverse Problems (:class:`~atommic.collections.reconstruction.nn.modl.MoDL`). Adjusted to optionally perform a data consistency step (Conjugate Gradient), as presented in [Aggarwal2018]_, [Yaman2020]_. If dc is set to False, the network will perform a simple residual learning step. References ---------- .. [Aggarwal2018] MoDL: Model Based Deep Learning Architecture for Inverse Problems by H.K. Aggarwal, M.P Mani, and Mathews Jacob in IEEE Transactions on Medical Imaging, 2018 .. [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uğurbil, K, Akçakaya, M. Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson Med. 2020; 84: 3172– 3191. https://doi.org/10.1002/mrm.28378 Example configuration: .. code-block:: bash model: model_name: MoDL unrolled_iterations: 5 residual_blocks: 5 channels: 64 regularization_factor: 0.1 penalization_weight: 1.0 conjugate_gradient_dc: false conjugate_gradient_iterations: 1 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false MultiDomainNet ~~~~~~~~~~~~~~ Feature-level multi-domain module. Inspired by AIRS Medical submission to the FastMRI 2020 challenge. Example configuration: .. code-block:: bash model: model_name: MultiDomainNet standardization: true num_filters: 64 num_pool_layers: 2 dropout_probability: 0.0 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false ProximalGradient ~~~~~~~~~~~~~~~~ Proximal/Conjugate Gradient (:class:`~atommic.collections.reconstruction.nn.proximal_gradient.ProximalGradient`), according to [Aggarwal2018]_, [Yaman2020]_. References ---------- .. [Aggarwal2018] MoDL: Model Based Deep Learning Architecture for Inverse Problems by H.K. Aggarwal, M.P Mani, and Mathews Jacob in IEEE Transactions on Medical Imaging, 2018 .. [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uğurbil, K, Akçakaya, M. Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson Med. 2020; 84: 3172– 3191. https://doi.org/10.1002/mrm.28378 Example configuration: .. code-block:: bash model: model_name: PROXIMALGRADIENT conjugate_gradient_dc: true conjugate_gradient_iterations: 10 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Recurrent Variational Network ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Recurrent Variational Network (:class:`~atommic.collections.reconstruction.nn.recurrentvarnet.RecurrentVarNet`), as presented in [Yiasemis2021]_. References ---------- .. [Yiasemis2021] Yiasemis, George, et al. “Recurrent Variational Network: A Deep Learning Inverse Problem Solver Applied to the Task of Accelerated MRI Reconstruction.” ArXiv:2111.09639 [Physics], Nov. 2021. arXiv.org, http://arxiv.org/abs/2111.09639. Example configuration: .. code-block:: bash model: model_name: RVN in_channels: 2 recurrent_hidden_channels: 64 recurrent_num_layers: 4 num_steps: 8 no_parameter_sharing: true learned_initializer: true initializer_initialization: "sense" initializer_channels: - 32 - 32 - 64 - 64 initializer_dilations: - 1 - 1 - 2 - 4 initializer_multiscale: 1 accumulate_predictions: false # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Recurrent Inference Machines ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Recurrent Inference Machines (:class:`~atommic.collections.reconstruction.nn.rim_base.rim_block.RIMBlock`), as presented in [Lonning19]_. References ---------- .. [Lonning19] Lønning K, Putzky P, Sonke JJ, Reneman L, Caan MW, Welling M. Recurrent inference machines for reconstructing heterogeneous MRI data. Medical image analysis. 2019 Apr 1;53:64-78. Example configuration: .. code-block:: bash model: model_name: CIRIM recurrent_layer: GRU conv_filters: - 64 - 64 - 2 conv_kernels: - 5 - 3 - 3 conv_dilations: - 1 - 2 - 1 conv_bias: - true - true - false recurrent_filters: - 64 - 64 - 0 recurrent_kernels: - 1 - 1 - 0 recurrent_dilations: - 1 - 1 - 0 recurrent_bias: - true - true - false depth: 2 time_steps: 8 conv_dim: 2 num_cascades: 1 no_dc: true keep_prediction: true accumulate_predictions: true # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false UNet ~~~~ UNet (:class:`~atommic.collections.reconstruction.nn.unet.UNet`), as presented in [Ronneberger2015]_. References ---------- .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. Example configuration: .. code-block:: bash model: model_name: UNet channels: 64 pooling_layers: 4 in_channels: 2 out_channels: 2 padding_size: 11 dropout: 0.0 normalize: true norm_groups: 2 # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Variable Splitting Network ~~~~~~~~~~~~~~~~~~~~~~~~~~ Variable Splitting Network (:class:`~atommic.collections.reconstruction.nn.vsnet.VSNet`), as presented in [Duan2019]_. References ---------- .. [Duan2019] Duan, J. et al. (2019) Vs-net: Variable splitting network for accelerated parallel MRI reconstruction, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11767 LNCS, pp. 713–722. doi: 10.1007/978-3-030-32251-9_78. Example configuration: .. code-block:: bash model: model_name: VSNet num_cascades: 10 imspace_model_architecture: CONV imspace_in_channels: 2 imspace_out_channels: 2 imspace_conv_hidden_channels: 64 imspace_conv_n_convs: 4 imspace_conv_batchnorm: false # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false XPDNet ~~~~~~ XPDNet (:class:`~atommic.collections.reconstruction.nn.xpdnet.XPDNet`), as presented in [Ramzi2021]_. References ---------- .. [Ramzi2021] Ramzi, Zaccharie, et al. “XPDNet for MRI Reconstruction: An Application to the 2020 FastMRI Challenge. ArXiv:2010.07290 [Physics, Stat], July 2021. arXiv.org, http://arxiv.org/abs/2010.07290. Example configuration: .. code-block:: bash model: model_name: XPDNet num_primal: 5 num_dual: 1 num_iter: 20 use_primal_only: true kspace_model_architecture: CONV kspace_in_channels: 2 kspace_out_channels: 2 dual_conv_hidden_channels: 16 dual_conv_num_dubs: 2 dual_conv_batchnorm: false image_model_architecture: MWCNN imspace_in_channels: 2 imspace_out_channels: 2 mwcnn_hidden_channels: 16 mwcnn_num_scales: 2 mwcnn_bias: true mwcnn_batchnorm: false normalize_image: false # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false Zero-Filled ~~~~~~~~~~~ Zero-Filled reconstruction using either root-sum-of-squares (RSS) or SENSE (SENSitivity Encoding, as presented in [Pruessmann1999]_). References ---------- .. [Pruessmann1999] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med 1999; 42:952-962. Example configuration: .. code-block:: bash model: model_name: ZF # task & dataset related parameters coil_combination_method: SENSE coil_dim: 1 complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false fft_normalization: backward spatial_dims: - -2 - -1 normalization_type: minmax unnormalize_loss_inputs: false unnormalize_log_outputs: false MRI Segmentation (SEG) ---------------------- Attention UNet ~~~~~~~~~~~~~~ Attention UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.attentionunet.SegmentationAttentionUNet`), as presented in [Oktay2018]_. References ---------- .. [Oktay2018] O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y. Hammerla, B. Kainz, B. Glocker, D. Rueckert. Attention U-Net: Learning Where to Look for the Pancreas. 2018. https://arxiv.org/abs/1804.03999 Example configuration: .. code-block:: bash model: model_name: SEGMENTATIONATTENTIONUNET segmentation_module: AttentionUNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_channels: 32 segmentation_module_pooling_layers: 5 segmentation_module_dropout: 0.0 # task & dataset related parameters coil_combination_method: SENSE # if complex data coil_dim: 1 # if complex data complex_data: true # or false if using magnitude data complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false # if complex data fft_normalization: backward # if complex data spatial_dims: - -2 # if complex data - -1 # if complex data magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false Dynamic UNet ~~~~~~~~~~~~ Dynamic UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.dynunet.SegmentationDYNUNet`), as presented in [Isensee2018]_. References ---------- .. [Isensee2018] Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G, Norajitra T, Wirkert S, Maier-Hein KH. nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486. 2018 Sep 27. Example configuration: .. code-block:: bash model: model_name: SEGMENTATIONDYNUNET segmentation_module: DYNUNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_channels: - 64 - 128 - 256 - 512 segmentation_module_kernel_size: - 3 - 3 - 3 - 1 segmentation_module_strides: - 1 - 1 - 1 - 1 segmentation_module_dropout: 0.0 segmentation_module_norm: instance segmentation_module_activation: leakyrelu segmentation_module_deep_supervision: true segmentation_module_deep_supervision_levels: 2 # task & dataset related parameters coil_combination_method: SENSE # if complex data coil_dim: 1 # if complex data complex_data: true # or false if using magnitude data complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false # if complex data fft_normalization: backward # if complex data spatial_dims: - -2 # if complex data - -1 # if complex data magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false Lambda UNet ~~~~~~~~~~~ Lambda UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.lambdaunet.SegmentationLambdaUNet`), as presented in [Yanglan2021]_. References ---------- .. [Yanglan2021] Yanglan Ou, Ye Yuan, Xiaolei Huang, Kelvin Wong, John Volpi, James Z. Wang, Stephen T.C. Wong. LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images. 2021. https://arxiv.org/abs/2104.13917 Example configuration: .. code-block:: bash model: model_name: SEGMENTATIONLAMBDAUNET segmentation_module: LambdaUNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_channels: 64 segmentation_module_pooling_layers: 2 segmentation_module_dropout: 0.0 segmentation_module_query_depth: 16 segmentation_module_intra_depth: 1 segmentation_module_receptive_kernel: 1 segmentation_module_temporal_kernel: 1 # task & dataset related parameters coil_combination_method: SENSE # if complex data coil_dim: 1 # if complex data complex_data: true # or false if using magnitude data complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false # if complex data fft_normalization: backward # if complex data spatial_dims: - -2 # if complex data - -1 # if complex data magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false UNet ~~~~ 2D UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.unet.SegmentationUNet`), as presented in [Ronneberger2015]_. References ---------- .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. Example configuration: .. code-block:: bash model: model_name: SEGMENTATIONUNET segmentation_module: UNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_channels: 64 segmentation_module_pooling_layers: 2 segmentation_module_dropout: 0.0 # task & dataset related parameters coil_combination_method: SENSE # if complex data coil_dim: 1 # if complex data complex_data: true # or false if using magnitude data complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false # if complex data fft_normalization: backward # if complex data spatial_dims: - -2 # if complex data - -1 # if complex data magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false UNet 3D ~~~~~~~ 3D UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.unet3d.Segmentation3DUNet`), as presented in [Ronneberger2015]_. References ---------- .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. Example configuration: .. code-block:: bash model: model_name: SEGMENTATION3DUNET segmentation_module: UNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_channels: 64 segmentation_module_pooling_layers: 2 segmentation_module_dropout: 0.0 # task & dataset related parameters coil_combination_method: SENSE # if complex data coil_dim: 1 # if complex data complex_data: true # or false if using magnitude data complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false # if complex data fft_normalization: backward # if complex data spatial_dims: - -2 # if complex data - -1 # if complex data magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false UNETR ~~~~~ UNETR for MRI segmentation (:class:`~atommic.collections.segmentation.nn.unetr.SegmentationUNetR`), as presented in [Hatamizadeh2022]_. References ---------- .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr: Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2022 (pp. 574-584). Example configuration: .. code-block:: bash model: model_name: SEGMENTATIONUNETR segmentation_module: UNETR segmentation_module_input_channels: 1 segmentation_module_output_channels: 3 segmentation_module_img_size: (256, 256) segmentation_module_channels: 64 segmentation_module_hidden_size: 768 segmentation_module_mlp_dim: 3072 segmentation_module_num_heads: 12 segmentation_module_pos_embed: conv segmentation_module_norm_name: instance segmentation_module_conv_block: true segmentation_module_res_block: true segmentation_module_dropout: 0.0 segmentation_module_qkv_bias: false # task & dataset related parameters coil_combination_method: SENSE # if complex data coil_dim: 1 # if complex data complex_data: true # or false if using magnitude data complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false # if complex data fft_normalization: backward # if complex data spatial_dims: - -2 # if complex data - -1 # if complex data magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false V-Net ~~~~~ V-Net for MRI segmentation (:class:`~atommic.collections.segmentation.nn.vnet.SegmentationVNet`), as presented in [Milletari2016]_. References ---------- .. [Milletari2016] Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, 2016. https://arxiv.org/abs/1606.04797 Example configuration: .. code-block:: bash model: use_reconstruction_module: false segmentation_module: VNet segmentation_module_input_channels: 1 segmentation_module_output_channels: 4 segmentation_module_activation: elu segmentation_module_dropout: 0.0 segmentation_module_bias: False segmentation_module_padding_size: 15 # task & dataset related parameters coil_combination_method: SENSE # if complex data coil_dim: 1 # if complex data complex_data: true # or false if using magnitude data complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs consecutive_slices: 1 dimensionality: 2 estimate_coil_sensitivity_maps_with_nn: false fft_centered: false # if complex data fft_normalization: backward # if complex data spatial_dims: - -2 # if complex data - -1 # if complex data magnitude_input: true normalization_type: minmax normalize_segmentation_output: true unnormalize_loss_inputs: false unnormalize_log_outputs: false