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MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation.

Emre Dandıl¹, Betül Tiryaki Baştuğ², Mehmet Süleyman Yıldırım³

¹Department of Computer Engineering, Faculty of Engineering, Bilecik Seyh Edebali University, 11230 Bilecik, Türkiye.

Diagnostics (Basel, Switzerland)

|November 9, 2024

Summary

This summary is machine-generated.

This study introduces MaskAppendix, a deep learning model for accurate appendix segmentation in CT scans. It achieves state-of-the-art results, improving diagnostic accuracy for appendicitis and related conditions.

Keywords:

CT imaging Detectron appendix segmentation deep learning grad-CAM mask R-CNN

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Medicine

Background:

Appendicitis is a common cause of emergency abdominal surgery, necessitating accurate diagnosis.
Automated appendix segmentation in medical imaging is challenging due to anatomical variability.

Purpose of the Study:

To develop a precise appendix segmentation method for computed tomography (CT) scans.
To enhance diagnostic accuracy and clinical workflow efficiency for appendicitis.

Main Methods:

Proposed a backbone-enriched Mask R-CNN architecture (MaskAppendix) using ResNet101.
Integrated Gradient-weighted Class Activation Mapping (Grad-CAM) for improved feature localization.
Utilized the Detectron platform for implementation.

Main Results:

Achieved state-of-the-art performance in appendix segmentation on abdominal CT scans.
Obtained a Dice Similarity Coefficient (DSC) score of 87.17% for automatic segmentation.
Demonstrated superior accuracy and robustness compared to traditional methods.

Conclusions:

The MaskAppendix framework offers an effective tool for aiding clinicians in diagnosing appendicitis.
The method has the potential to reduce diagnostic errors and improve clinical workflow.
This AI-driven approach enhances the precision of medical image analysis for abdominal conditions.