Improving Lesion Segmentation in FDG-18 Whole-Body PET/CT scans using
  Multilabel approach: AutoPET II challenge
Gowtham Krishnan Murugesan Diana McCrumb Eric Brunner Jithendra Kumar Rahul Soni Vasily Grigorash Stephen Moore Jeff Van Oss
Automatic segmentation of lesions in FDG-18 Whole Body (WB) PET/CT scans
using 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 the
liver, spleen, brain, and bladder, often leads to challenges, as these regions
are often misidentified as lesions by deep learning models. To address this
issue, we propose a novel approach of segmenting both organs and lesions,
aiming to enhance the performance of automatic lesion segmentation methods. In
this study, we assessed the effectiveness of our proposed method using the
AutoPET II challenge dataset, which comprises 1014 subjects. We evaluated the
impact of inclusion of additional labels and data in the segmentation
performance of the model. In addition to the expert-annotated lesion labels, we
introduced eight additional labels for organs, including the liver, kidneys,
urinary bladder, spleen, lung, brain, heart, and stomach. These labels were
integrated into the dataset, and a 3D UNET model was trained within the nnUNet
framework. Our results demonstrate that our method achieved the top ranking in
the held-out test dataset, underscoring the potential of this approach to
significantly improve lesion segmentation accuracy in FDG-18 Whole-Body PET/CT
scans, ultimately benefiting cancer patients and advancing clinical practice.