Artificial Intelligence

    

AutoPET III Challenge: PET/CT Semantic Segmentation

Authors: Reza Safdari, Mohammad Koohi-Moghaddam, Kyongtae Tyler Bae

In this study, we implemented a two-stage deep learning-based approach to segmentlesions in PET/CT images for the AutoPET III challenge. The first stage utilized aDynUNet model for coarse segmentation, identifying broad regions of interest. Thesecond stage refined this segmentation using an ensemble of SwinUNETR, SegResNet,and UNet models. Preprocessing involved resampling images to a common resolution andnormalization, while data augmentation techniques such as affine transformations andintensity adjustments were applied to enhance model generalization. The dataset was splitinto 80% training and 20% validation, excluding healthy cases. This method leveragesmulti-stage segmentation and model ensembling to achieve precise lesion segmentation,aiming to improve robustness and overall performance.

Comments: 7 Pages.

Download: PDF

Submission history

[v1] 2024-09-20 04:44:00

Unique-IP document downloads: 166 times

Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus