RACLAHE_Image_Enhancement_for_CNN_model_segmentation

Region Adaptive Magnetic Resonance Image Enhancement for improving CNN based segmentation of the prostate and prostatic zones

The current repo supports the Nature’s Scientific report original publication
“Region-Adaptive Magnetic Resonance Image Enhancement for improving CNN-based segmentation of the prostate and prostatic zones”, doi:10.1038/s41598-023-27671-8

The algorithm has been implemented to enhance the prostate’s Whole Gland in T2-Weighted MR images. The repo contains 2 packages

Reference

Please cite our work if youu find it valuable for your experiments. Thank you!

@article{zaridis2023region,
  title={Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones},
  author={Zaridis, Dimitrios I and Mylona, Eugenia and Tachos, Nikolaos and Pezoulas, Vasileios C and Grigoriadis, Grigorios and Tsiknakis, Nikos and Marias, Kostas and Tsiknakis, Manolis and Fotiadis, Dimitrios I},
  journal={Scientific Reports},
  volume={13},
  number={1},
  pages={714},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

Zaridis, Dimitrios I., et al. “Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones.” Scientific Reports 13.1 (2023): 714.

Installation

First way of installation

Second way of installation

In order to Utilize the project

Abstract

Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models’ predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate’s gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3% to 9% for the different prostatic regions, while achieving minimal inter-model variability. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images.

Study’s Results

Several Results are shown below regarding the effectiveness of the proposed method vs various image enhancement methods in a single deep learning model Specifically, fig below indicate how the proposed image enhancement method effectively assist the USE-Net model to identify important for the model’s decision features. Raclahe filter

Moreover the proposed method’s methodology is presented below

Raclahe filter

Acknowledgements

Authors

Badges

MIT License Python

ProCAncer AI

Usage/Examples

pat_name = “Example” w_p = “Path/to/Weights/bbox_weights.h5” path_inp = “Path/To/Folder” # Folder includes patients in nifti format user_output = “Path/To/Save_Folder” # folder to save the Raclahe operation outcome in nifti format Raclahe_enhanced_patients = Raclahe_process_nifti(pat_name,w_p,path_inp,user_output)


 - An example for training your own region proposal model on your dataset could be found at the Demo_Training_Process.ipynb also
```python
from Training_Utils import * # import the package

pats = nib.load("Path/to/patients")
labs = nib.load("Path/to/labels")

pats_process = Bounding_Box_Operations.Bounding_Box_Preprocessing(pats,labs).resize(256,256,anno=False) # resize to 256x256
pats_process = Bounding_Box_Operations.Bounding_Box_Preprocessing(pats_process,labs).norm8bit() # Normalize to 8Bit
pats_process = Bounding_Box_Operations.Bounding_Box_Preprocessing(pats_process,labs).Standardization(min_max=True) # Standardize for the training process
pats_process,labs_process,_,bbox = Bounding_Box_Operations.Bounding_Box_Preprocessing(pats_process,labs).bounding_box_creation(size=30,path="",extract_bounding=False) # creation of the bounding box
bbox = Bounding_Box_Operations.Bounding_Box_Preprocessing(_,bbox).resize(256,256,anno=True) # resize the bounding box to 256x256

Bounding_Box_Operations.model_training(data_train=pats[:300],labels_train=bbox[:300],
                                       data_val=pats[300:350],labels_val=bbox[300:350],
                                       save_weights_path="Path/To/Save_the_Weights")
This site is open source. Improve this page.