MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation
Background: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/14/21/2346 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846173514921410560 |
|---|---|
| author | Emre Dandıl Betül Tiryaki Baştuğ Mehmet Süleyman Yıldırım Kadir Çorbacı Gürkan Güneri |
| author_facet | Emre Dandıl Betül Tiryaki Baştuğ Mehmet Süleyman Yıldırım Kadir Çorbacı Gürkan Güneri |
| author_sort | Emre Dandıl |
| collection | DOAJ |
| description | Background: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other anatomical structures. Methods: In this study, we propose a backbone-enriched Mask R-CNN architecture (MaskAppendix) on the Detectron platform, enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM), for precise appendix segmentation on computed tomography (CT) scans. In the proposed MaskAppendix deep learning model, ResNet101 network is used as the backbone. By integrating Grad-CAM into the MaskAppendix network, our model improves feature localization, allowing it to better capture subtle variations in appendix morphology. Results: We conduct extensive experiments on a dataset of abdominal CT scans, demonstrating that our method achieves state-of-the-art performance in appendix segmentation, outperforming traditional segmentation techniques in terms of both accuracy and robustness. In the automatic segmentation of the appendix region in CT slices, a DSC score of 87.17% was achieved with the proposed approach, and the results obtained have the potential to improve clinical diagnostic accuracy. Conclusions: This framework provides an effective tool for aiding clinicians in the diagnosis of appendicitis and other related conditions, reducing the potential for diagnostic errors and enhancing clinical workflow efficiency. |
| format | Article |
| id | doaj-art-344d3ec54c134c9c82ba6d69f5c91cf7 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-344d3ec54c134c9c82ba6d69f5c91cf72024-11-08T14:34:37ZengMDPI AGDiagnostics2075-44182024-10-011421234610.3390/diagnostics14212346MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix SegmentationEmre Dandıl0Betül Tiryaki Baştuğ1Mehmet Süleyman Yıldırım2Kadir Çorbacı3Gürkan Güneri4Department of Computer Engineering, Faculty of Engineering, Bilecik Seyh Edebali University, 11230 Bilecik, TürkiyeRadiology Department, Faculty of Medicine, Bilecik Şeyh Edebali University, 11230 Bilecik, TürkiyeDepartment of Söğüt Vocational School, Computer Technology, Bilecik Şeyh Edebali University, Söğüt, 11600 Bilecik, TürkiyeGeneral Surgery Department, Bilecik Osmaneli Mustafa Selahattin Çetintaş Hospital, 11500 Bilecik, TürkiyeGeneral Surgery Department, Faculty of Medicine, Bilecik Şeyh Edebali University, 11230 Bilecik, TürkiyeBackground: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other anatomical structures. Methods: In this study, we propose a backbone-enriched Mask R-CNN architecture (MaskAppendix) on the Detectron platform, enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM), for precise appendix segmentation on computed tomography (CT) scans. In the proposed MaskAppendix deep learning model, ResNet101 network is used as the backbone. By integrating Grad-CAM into the MaskAppendix network, our model improves feature localization, allowing it to better capture subtle variations in appendix morphology. Results: We conduct extensive experiments on a dataset of abdominal CT scans, demonstrating that our method achieves state-of-the-art performance in appendix segmentation, outperforming traditional segmentation techniques in terms of both accuracy and robustness. In the automatic segmentation of the appendix region in CT slices, a DSC score of 87.17% was achieved with the proposed approach, and the results obtained have the potential to improve clinical diagnostic accuracy. Conclusions: This framework provides an effective tool for aiding clinicians in the diagnosis of appendicitis and other related conditions, reducing the potential for diagnostic errors and enhancing clinical workflow efficiency.https://www.mdpi.com/2075-4418/14/21/2346appendix segmentationdeep learningCT imagingmask R-CNNgrad-CAMDetectron |
| spellingShingle | Emre Dandıl Betül Tiryaki Baştuğ Mehmet Süleyman Yıldırım Kadir Çorbacı Gürkan Güneri MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation Diagnostics appendix segmentation deep learning CT imaging mask R-CNN grad-CAM Detectron |
| title | MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation |
| title_full | MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation |
| title_fullStr | MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation |
| title_full_unstemmed | MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation |
| title_short | MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation |
| title_sort | maskappendix backbone enriched mask r cnn based on grad cam for automatic appendix segmentation |
| topic | appendix segmentation deep learning CT imaging mask R-CNN grad-CAM Detectron |
| url | https://www.mdpi.com/2075-4418/14/21/2346 |
| work_keys_str_mv | AT emredandıl maskappendixbackboneenrichedmaskrcnnbasedongradcamforautomaticappendixsegmentation AT betultiryakibastug maskappendixbackboneenrichedmaskrcnnbasedongradcamforautomaticappendixsegmentation AT mehmetsuleymanyıldırım maskappendixbackboneenrichedmaskrcnnbasedongradcamforautomaticappendixsegmentation AT kadircorbacı maskappendixbackboneenrichedmaskrcnnbasedongradcamforautomaticappendixsegmentation AT gurkanguneri maskappendixbackboneenrichedmaskrcnnbasedongradcamforautomaticappendixsegmentation |