Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study

Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study

Abstract This study aimed to develop a versatile automatic segmentation model of bladder cancer (BC) on MRI using a convolutional neural network and investigate the robustness of radiomics features automatically extracted from apparent diffusion coefficient (ADC) maps. This two-center retrospective...

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Bibliographic Details
Main Authors: Yusaku Moribata, Yasuhisa Kurata, Mizuho Nishio, Aki Kido, Satoshi Otani, Yuki Himoto, Naoko Nishio, Akihiro Furuta, Hiroyuki Onishi, Kimihiko Masui, Takashi Kobayashi, Yuji Nakamoto
Format: Article
Language:English
Published: Nature Portfolio 2023-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-27883-y

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https://doi.org/10.1038/s41598-023-27883-y

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