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Encoder-Decoder Variant Analysis for Semantic Segmentation of Gastrointestinal Tract Using UW-Madison Dataset.

Neha Sharma¹, Sheifali Gupta¹, Dalia H Elkamchouchi²

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India.

Bioengineering (Basel, Switzerland)

|March 28, 2025

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This study developed an AI model for segmenting gastrointestinal organs in MRI scans, improving precision for radiation therapy in GI cancer treatment. The best model, ResNet50 with DeepLab V3+, achieved high accuracy in organ segmentation.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Gastrointestinal (GI) tract cancer poses a significant global health challenge.
Accurate segmentation of GI organs is critical for effective radiation therapy planning.
Current methods require precise delineation of the stomach, small bowel, and large bowel to minimize damage to healthy tissues.

Purpose of the Study:

To explore and identify optimal encoder-decoder architectures for segmenting GI organs in MRI images.
To evaluate the performance of various deep learning models for precise organ segmentation.
To enhance the accuracy of radiation targeting in GI cancer treatment through improved image segmentation.

Main Methods:

Utilized the UW-Madison GI tract dataset comprising 38,496 MRI scans.

Keywords:

DeepLab V3+ResNet 50 UW-Madison GI dataset cancer decoders encoders gastrointestinal tract segmentation

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Tested multiple encoder networks (ResNet50, EfficientNetB1, MobileNetV2, ResNext50, Timm_Gernet_S).

Paired encoders with decoders including UNet, FPN, PSPNet, PAN, and DeepLab V3+ for segmentation.

Main Results:

The combination of ResNet50 encoder and DeepLab V3+ decoder demonstrated superior performance.
Achieved a Dice coefficient of 0.9082 and a Jaccard index (IoU) of 0.8796.
The model yielded a low model loss of 0.117, indicating high segmentation accuracy.

Conclusions:

The ResNet50-DeepLab V3+ model offers a highly effective solution for GI organ segmentation in MRI.
This approach has the potential to significantly improve radiation therapy planning and delivery for GI cancer patients.
Findings support the integration of advanced AI in biomedical image analysis for clinical applications.