Paper on H&N segmentation accepted at EMBC 2022

Our paper on “Segmentation and Classification of Head and Neck Nodal Metastases and Primary Tumors in PET/CT”, by V. Andrearczyk, V. Oreiller, M. Jreige, J.Castelli, J. O. Prior and A. Depeursinge has been accepted for presentation at the IEEE International Engineering in Medicine and Biology Conference (EMBC), held in Glasgow, 11-15 July 2022.

The prediction of cancer characteristics, treatment planning and patient outcome from medical images generally requires tumor delineation. In Head and Neck cancer (H\&N), the automatic segmentation and differentiation of primary Gross Tumor Volumes (GTVt) and malignant lymph nodes (GTVn) is a necessary step for large-scale radiomics studies. We developed a bi-modal 3D U-Net model to automatically individually segment GTVt and GTVn in PET/CT images. The model is trained for multi-class and multi-components segmentation on the multi-centric HECKTOR 2020 dataset.

Illustrations of 2D PET (SUV scale 0-7 mg/L) and CT slices overlayed with GTVt (blue) and GTVn (red) automatic segmentation. The corresponding DSC are reported in the captions. The ground truth is in bright color, the prediction in dark color. (a,b) Correctly detected and segmented GTVt and GTVn; (c) One GTVn correctly segmented (right), one largely undersegmented (left); (d) GTVn misclassified as a GTVt. Standard DSC is reported for each case to evaluate the 3D segmentation of GTVt and GTVn.