Deep Learning-Based Cascade 3D Kidney Segmentation Method.
Authors
Hao Z,Chapman BE
Affiliations (2)
Affiliations (2)
- Computing and Information Systems School, University of Melbourne, VIC, Australia.
- Melbourne Medical School, University of Melbourne, VIC, Australia.
Abstract
Renal tumors require early diagnosis and precise localization for effective treatment. This study aims to automate renal tumor analysis in abdominal CT images using a cascade 3D U-Net architecture for semantic kidney segmentation. To address challenges like edge detection and small object segmentation, the framework incorporates residual blocks to enhance convergence and efficiency. Comprehensive training configurations, preprocessing, and postprocessing strategies were employed to ensure accurate results. Tested on KiTS2019 data, the method ranked 23rd on the leaderboard (Nov 2024), demonstrating the enhanced cascade 3D U-Net's effectiveness in improving segmentation precision.
Topics
Deep LearningTomography, X-Ray ComputedImaging, Three-DimensionalKidney NeoplasmsKidneyRadiographic Image Interpretation, Computer-AssistedJournal Article