BowelNet

Pytorch code for the paper "BowelNet: Joint Semantic-Geometric Ensemble Learning for Bowel Segmentation from Both Partially and Fully Labeled CT Images" at IEEE TMI 2022.

Email: chong.wang@adelaide.edu.au

Introduction:

The BowelNet is a two-stage coarse-to-fine framework for the sgmentation of entire bowel (i.e., duodenum, jejunum-ileum, colon, sigmoid, and rectum) from abdominal CT images. The first stage jointly localizes all types of the bowel, trained robustly on both partially and fully labeled samples (see examples below). The second stage finely segments each type of localized the bowels using geometric bowel representations and hybrid psuedo labels:

(1) Joint localzation of the five bowel parts using both partially- and fully-labeled images

(2) Fine segmentation of each part using geometric (i.e., boundary and skeleton) guidance

Examples of fully (a) and partially (b, c) labeled training data used in our work:

Dataset:

We utilize a large private abdominal CT dataset that includes both partially and fully labeled segmentation annotations. The dataset is structured as follows:

BowelSegData
	├── Fully_labeled_5C
	│	├── abdomen
	│	│   ├── <patient_1>.nii.gz
	│	│   ...
	│	├── male
	│	│   ├── <patient_1>.nii.gz
	│	│   ...
	│	└── female
	│	    ├── <patient_1>.nii.gz
	│	    ...
	├── Colon_Sigmoid
        │	├── abdomen
	│	│   ├── <patient_1>.nii.gz
	│	│   ...
	│	├── male
	│	│   ├── <patient_1>.nii.gz
	│	│   ...
	│	└── female
	│	    ├── <patient_1>.nii.gz
	│	    ...
	└── Smallbowel
	 	├── abdomen
	 	│   ├── <patient_1>.nii.gz
	 	│   ...
	 	├── male
	 	│   ├── <patient_1>.nii.gz
	  	│   ...
	 	└── female
	 	    ├── <patient_1>.nii.gz
	 	    ...

Data Preprocessing:

Preprocessing includes cropping abdominal body region. We average all 2D CT slices of a volume to form a mean image and then apply a thresholding on it to obtain the abdominal body region (excluding CT bed).

Demo segmentation:

Our BowelNet demonstrates improved performance over prior approaches in the entire bowel segmentation.

Citation:

@article{wang2022bowelnet,
  title={BowelNet: Joint Semantic-Geometric Ensemble Learning for Bowel Segmentation From Both Partially and Fully Labeled CT Images},
  author={Wang, Chong and Cui, Zhiming and Yang, Junwei and Han, Miaofei and Carneiro, Gustavo and Shen, Dinggang},
  journal={IEEE Transactions on Medical Imaging},
  volume={42},
  number={4},
  pages={1225--1236},
  year={2022},
  publisher={IEEE}
}