Article

Assessing Quantitative Performance and Expert Review of Multiple Deep Learning-Based Frameworks for Computed Tomography-based Abdominal Organ Auto-Segmentation

2024-10-02

Abstract excerpt

<h4>ABSTRACT</h4> Segmentation of abdominal organs in clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up. However, manually generated segmentations are time-consuming and labor-intensive in addition to experiencing inter-observer variability. Many deep learning (DL) and Automated Machine Learning (AutoML) frameworks have emerged as a solution to this challenge and sho...

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Literature Corpus work: c6af5c24-1a32-5325-adb7-802edbff585f
DOI: 10.1101/2024.10.02.24312658

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