Work

Kidney Tumor Segmentation Using a Hybrid CNN-Transformer Network for Partial Nephrectomy Planning

Year: 2024

Type: article

Abstract: In partial nephrectomy for kidney cancer treatment, accurate segmentation of the kidney tumor is crucial for surgical planning, as it provides essential information on the precise size and location of the tumor. However, it is challenging due to the tumor’s similar intensity to surrounding organs and the variability in its location and size across patients. In this study, we propose a hybrid network that integrates a convolutional neural network and a transformer to capture both local and global features, aiming to improve the segmentation performance of kidney tumors. We validated our method through comparative experiments with UNETR++, outperforming it with a Dice Similarity Coefficient (DSC) of 78.54% and a precision of 85.07%. Moreover, in the analysis by tumor size, our method demonstrated improvements by reducing over-segmentation and outlier cases observed in UNETR++.

Source: Journal of the Korea Computer Graphics Society

DOI: 10.15701/kcgs.2024.30.4.11

Authors: Goun Kim, Jinseo An, Yubeen Lee, Helen Hong

Institution: Seoul Women's University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Seoul Women`s University, Ministry of Health and Welfare