Abstract
The accurate, automated detection and segmentation of renal tumors is of great interest for the imaging-based diagnosis, histologic subtyping, and management of suspected renal malignancy. The KiTS21 Grand Challenge provides 300 contrast enhanced CT images with kidney, tumors and cysts with corresponding manual annotation, to facilitate the development of robust segmentation algorithms for this task. In this work, we present an adaptation of the historically-successful 3D U-Net architecture, combined with deep supervision, foreground oversampling and large-scale image context, and trained on the majority-prediction segmentation masks. Our model achieved test-set performance of 97.0%, 85.1%, and 81.9% volumetric Dice score, and 93.7%, 72.0%, and 70.0% surface Dice score, on combined foreground, renal masses, and renal tumors, respectively, which tied for sixth place among challenge participants.
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Zang, M., Wysoczanski, A., Angelini, E., Laine, A.F. (2022). 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds) Kidney and Kidney Tumor Segmentation. KiTS 2021. Lecture Notes in Computer Science, vol 13168. Springer, Cham. https://doi.org/10.1007/978-3-030-98385-7_19
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DOI: https://doi.org/10.1007/978-3-030-98385-7_19
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