Technical report: Kidney tumor segmentation using a 2D U-Net followed by a statistical post-processing filter

Autor:	Paolucci, Iwan
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Each year, there are about 400'000 new cases of kidney cancer worldwide causing around 175'000 deaths. For clinical decision making it is important to understand the morphometry of the tumor, which involves the time-consuming task of delineating tumor and kidney in 3D CT images. Automatic segmentation could be an important tool for clinicians and researchers to also study the correlations between tumor morphometry and clinical outcomes. We present a segmentation method which combines the popular U-Net convolutional neural network architecture with post-processing based on statistical constraints of the available training data. The full implementation, based on PyTorch, and the trained weights can be found on GitHub. Comment: KiTS 2019 challenge
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.10727 Zobrazit plný text záznamu View this record from Arxiv