An Ensemble of 2.5D ResUnet Based Models for Segmentation for Kidney and Masses

2023/11/27 by Cancan Chen, Chen, Cancan, RongguoZhang
Computer Science · Medicine · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Renal cell carcinoma treatment #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2311.15586

openalex publication_date 2023/11/27 · openalex created_date 2023/11/29 · openalex updated_date 2026/07/28

Abstract

The automatic segmentation of kidney, kidney tumor and kidney cyst on Computed Tomography (CT) scans is a challenging task due to the indistinct lesion boundaries and fuzzy texture. Considering the large range and unbalanced distribution of CT scans' thickness, 2.5D ResUnet are adopted to build an efficient coarse-to-fine semantic segmentation framework in this work. A set of 489 CT scans are used for training and validation, and an independent never-before-used CT scans for testing. Finally, we demonstrate the effectiveness of our proposed method. The dice values on test set are 0.954, 0.792, 0.691, the surface dice values are 0.897, 0.591, 0.541 for kidney, tumor and cyst, respectively. The average inference time of each CT scan is 20.65s and the max GPU memory is 3525MB. The results suggest that a better trade-off between model performance and efficiency.

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