A Robust Network for Multi-Label Abdominal Organs Segmentation
| Autor: | Huu Sy Le, Kha Tu Huynh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Vietnam Journal of Computer Science, Vol 11, Iss 03, Pp 377-410 (2024) |
| Druh dokumentu: | article |
| ISSN: | 21968888 2196-8896 2196-8888 |
| DOI: | 10.1142/S2196888824500076 |
| Popis: | The paper proposes a robust and efficient model designed for multi-label abdominal organ segmentation, featuring a substantially reduced number of parameters. The model focuses on the effectiveness of edge guidance in segmentation and leverages a 3D-Unet architecture with deep supervision, incorporating the robust deep thinking gate (DTG) architecture. Our DTG-incorporated model architecture excels in both efficiency and effectiveness, demonstrating notable enhancements in multi-label abdominal organ segmentation performance. A comprehensive evaluation of the model employed on two datasets of MRI scan of BTCV and FLARE 2022, comparing its performance against state-of-the-art counterparts. The outcomes revealed that the proposed model achieved the highest dice score in the esophagus (0.795), gallbladder (0.945), and pancreas (0.87) while maintaining a most significantly reduced parameter count (13.3 million parameters count). This achievement underscores the model’s efficiency and its suitability for seamless integration into real-world applications, offering promising prospects for enhanced medical image analysis. |
| Databáze: | Directory of Open Access Journals |
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