A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images
This work addresses the problem of reducing doctors' workload and improving diagnostic quality for cancer detection, but it appears incremental as it builds on existing segmentation methods with a specific framework.
The paper tackles the challenge of automatic tumor segmentation in whole-body PET/CT images by proposing a localization-to-segmentation framework (L2SNet) with an adaptive threshold scheme, achieving a competitive result ranked in the top 7 methods on the MICCAI 2023 challenge dataset.
Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography (CT) is considered the primary solution for detecting some cancers, such as lung cancer and melanoma. Automatic segmentation of tumors in PET/CT images can help reduce doctors' workload, thereby improving diagnostic quality. However, precise tumor segmentation is challenging due to the small size of many tumors and the similarity of high-uptake normal areas to the tumor regions. To address these issues, this paper proposes a localization-to-segmentation framework (L2SNet) for precise tumor segmentation. L2SNet first localizes the possible lesions in the lesion localization phase and then uses the location cues to shape the segmentation results in the lesion segmentation phase. To further improve the segmentation performance of L2SNet, we design an adaptive threshold scheme that takes the segmentation results of the two phases into consideration. The experiments with the MICCAI 2023 Automated Lesion Segmentation in Whole-Body FDG-PET/CT challenge dataset show that our method achieved a competitive result and was ranked in the top 7 methods on the preliminary test set. Our work is available at: https://github.com/MedCAI/L2SNet.