Deep Implicit Statistical Shape Models for 3D Medical Image Delineation

Please find the paper at DISSM

Training requires the MONAI library

Pre-requisties

There are three main steps to using this system:

1. Create a library of shapes from a set of masks. Please see CONVERT.md.

2. Train a deep SDF auto-decoded model based off the library of shapes. Please see DEEPSDF.md.

3. Now train an encoder to estimate an organ's shape given an image.

Training DISM

To train DISM, we use MSL strategy. First we train Translation model with bigger resolution and then use the translated shape to crop and then perform subsequent models with the cropped resolution.

Training Translation

See PREDICT_TRANSLATION.md

Training script for scale

python train_episodic.py --im_root /data/decathalon/Task03_Liver/latest_update_october/imagesTr_resampled_pad_crop_new/  
--train_json_list ../data/liver_optimize_sdf/ --val_json_list ../data/liver_optimize_sdf/ 
--yaml_file config_predict_48.yml 
--embed_model_path /data/nas/Projects/StasticalShapeModel/ShapeEmbed/ckpts/Liver0.2_jitter/last_checkpoint.ckpt 
--mean_sdf_file /data/decathalon/Task03_Liver/latest_update_october/mean_centred_sdf_crop.nii.gz 
--embed_yaml_file config.yml --save_path /data/results/liver/optimize_through_sdf/resize_4_4_4/episodic_training_step_15_trans_scale_crop_v1 
--sdf_sample_root /data/decathalon/Task03_Liver/latest_update_october/labelsTr_crop_samples/ --resume /data/results/liver/optimize
_through_sdf/resize_4_4_4/episodic_training_step_7_trans/ckpt/best_model.pth --do_scale

Necessary files/things to have for training scale model.

1. Unlike Translation, for scale we need to crop the data so that we can focus on the organ alone.

   Steps to crop the organ using the trained translation model.

Training script for rotation

python train_episodic.py --im_root /data/decathalon/Task03_Liver/latest_update_october/imagesTr_resampled_pad_crop_new/  
--train_json_list ../data/liver_optimize_sdf/ --val_json_list ../data/liver_optimize_sdf/ 
--yaml_file config_predict_48.yml 
--embed_model_path /data/nas/Projects/StasticalShapeModel/ShapeEmbed/ckpts/Liver0.2_jitter/last_checkpoint.ckpt 
--mean_sdf_file /data/decathalon/Task03_Liver/latest_update_october/mean_centred_sdf_crop.nii.gz 
--embed_yaml_file config.yml --save_path /data/results/liver/optimize_through_sdf/resize_4_4_4/episodic_training_step_15_trans_scale_crop_v1 
--sdf_sample_root /data/decathalon/Task03_Liver/latest_update_october/labelsTr_crop_samples/ --resume /data/results/liver/optimize
_through_sdf/resize_4_4_4/episodic_training_step_7_trans/ckpt/best_model.pth --do_scale --do_rotate

Training script for PCA

python train_episodic.py --im_root /data/decathalon/Task03_Liver/latest_update_october/imagesTr_resampled_pad_crop_new/  
--train_json_list ../data/liver_optimize_sdf/ --val_json_list ../data/liver_optimize_sdf/ 
--yaml_file config_predict_48.yml 
--embed_model_path /data/nas/Projects/StasticalShapeModel/ShapeEmbed/ckpts/Liver0.2_jitter/last_checkpoint.ckpt 
--mean_sdf_file /data/decathalon/Task03_Liver/latest_update_october/mean_centred_sdf_crop.nii.gz 
--embed_yaml_file config.yml --save_path /data/results/liver/optimize_through_sdf/resize_4_4_4/episodic_training_step_15_trans_scale_crop_v1 
--sdf_sample_root /data/decathalon/Task03_Liver/latest_update_october/labelsTr_crop_samples/ --resume /data/results/liver/optimize
_through_sdf/resize_4_4_4/episodic_training_step_7_trans/ckpt/best_model.pth --do_scale --do_rotate --do_pca --pca_components liver_pca_28.npy