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Analysis of lymph node tumor features in PET/CT for segmentation

Abstract : In the context of breast cancer, the detection and segmentation of cancerous lymph nodes in PET/CT imaging is of crucial importance, in particular for staging issues. In order to guide such image analysis procedures, some dedicated descriptors can be considered, especially region-based features. In this article, we focus on the issue of choosing which features should be embedded for lymph node tumor segmentation from PET/CT. This study is divided into two steps. We first investigate the relevance of various features by considering a Random Forest framework. In a second time, we validate the expected relevance of the best scored features by involving them in a U-Net segmentation architecture. We handle the region-based definition of these features thanks to a hierarchical modeling of the PET images. This analysis emphasizes a set of features that can significantly improve / guide the segmentation of lymph nodes in PET/CT.
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Contributor : Nicolas Passat <>
Submitted on : Monday, April 12, 2021 - 1:57:18 PM
Last modification on : Tuesday, April 20, 2021 - 10:40:25 AM


  • HAL Id : hal-03104417, version 1


Diana Lucia Farfan Cabrera, Eloïse Grossiord, Nicolas Gogin, Dimitri Papathanassiou, Nicolas Passat. Analysis of lymph node tumor features in PET/CT for segmentation. International Symposium on Biomedical Imaging (ISBI), Apr 2021, Nice (en ligne), France. ⟨hal-03104417⟩



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