A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images

arXiv:2405.01644v1 Announce Type: cross
Abstract: Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility of building a workflow to efficiently route images to specifically trained segmentation models. By implementing a deep learning classifier to automatically classify the images and route them to appropriate segmentation models, we hope that our workflow can segment the images with different pathology accurately. The data we used in …

abstract accuracy adaptability arxiv automated building cancer classification cs.cv disease eess.iv explore images pathology physics.med-ph pipeline route segmentation study tools type workflow