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Automatic segmentation of lesions in FDG-18 Whole Body (WB) PET/CT scansusing deep learning models is instrumental for determining treatment response,optimizing dosimetry, and advancing theranostic applications in oncology.However, the presence of organs with elevated radiotracer uptake, such as theliver, spleen, brain, and bladder, often leads to challenges, as these regionsare often misidentified as lesions by deep learning models. To address thisissue, we propose a novel approach of segmenting both organs and lesions,aiming to enhance the performance of automatic lesion segmentation methods. Inthis study, we assessed the effectiveness of our proposed method using theAutoPET II challenge dataset, which comprises 1014 subjects. We evaluated theimpact of inclusion of additional labels and data in the segmentationperformance of the model. In addition to the expert-annotated lesion labels, weintroduced eight additional labels for organs, including the liver, kidneys,urinary bladder, spleen, lung, brain, heart, and stomach. These labels wereintegrated into the dataset, and a 3D UNET model was trained within the nnUNetframework. Our results demonstrate that our method achieved the top ranking inthe held-out test dataset, underscoring the potential of this approach tosignificantly improve lesion segmentation accuracy in FDG-18 Whole-Body PET/CTscans, ultimately benefiting cancer patients and advancing clinical practice.

Improving Lesion Segmentation in FDG-18 Whole-Body PET/CT scans using Multilabel approach: AutoPET II challenge