Supervised detection and segmentation of metastatic cancer lesions is an area of active research in medical imaging, including targeted PET\/CT imaging of prostate-specific membrane antigen (PSMA). However, due to the unpredictable location of metastasis occurrence, supervised learning methods may require very large collections of segmented images to achieve high levels of performance. Building such datasets requires significant time and resources. Alternatively, we aimed to develop a novel unsupervised framework for subtracting healthy tracer uptake patterns via deep learning and dual-modality PET\/CT guidance, with application to PSMA PET imaging. After the removal of normal background, cancer metastases become more prominent in the residual images. Our method does not require existing lesion segmentations and can leverage lesion-negative images.<\/p>\n","protected":false},"excerpt":{"rendered":"
Supervised detection and segmentation of metastatic cancer lesions is an area of active research in medical imaging, including targeted PET\/CT imaging of prostate-specific membrane antigen (PSMA). However, due to the unpredictable location of metastasis occurrence, supervised learning methods may require very large collections of segmented images to achieve high levels of performance. 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