Disentangled PET Lesion Segmentation
PET imaging is an invaluable tool in clinical settings as it captures the functional activity of both healthy anatomy and cancerous lesions. Developing automatic lesion segmentation methods for PET images is crucial since manual lesion segmentation is laborious and prone to inter- and intra-observer variability. We propose PET-Disentangler, a 3D disentanglement method that uses a 3D UNet-like encoder-decoder architecture to disentangle disease and normal healthy anatomical features with losses for segmentation, reconstruction, and healthy component plausibility. A critic network is used to encourage the healthy latent features to match the distribution of healthy samples and thus encourages these features to not contain any lesion-related features. Our quantitative results show that PET-Disentangler is less prone to incorrectly declaring healthy and high tracer uptake regions as cancerous lesions, since such uptake pattern would be assigned to the disentangled healthy component.
Code (0)
등록된 구현이 없습니다.
Tasks
AnatomyDecoderDisentanglementLesion SegmentationSegmentationSimilar Papers 제목 키워드 기반
View-Disentangled Transformer for Brain Lesion Detection
Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation. However, locating small lesions in 2D MRI slices is challenging, and requires to balance between the granularity of 3D cont…
Lesion DetectionSD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT
Optical coherence tomography (OCT) is widely used for diagnosing and monitoring retinal diseases, such as age-related macular degeneration (AMD). The segmentation of biomarkers such as layers and lesions is essential for…
Lesion SegmentationFedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation
Federated Learning (FL) in Deep Learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. However, FL faces challenges with data he…
DisentanglementFederated LearningImage SegmentationMedical Image Segmentation+3Physics-Grounded Disentangled Flow Modeling for Brain Disease Progression Trajectory
Forecasting longitudinal brain lesion evolution is critical for disease monitoring and treatment planning. Existing approaches typically learn a direct mapping from a baseline image to a future observation, without expli…
Trajectory ForecastingDense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation
One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many …
Lesion SegmentationMedical Image AnalysisSegmentationSkin Lesion Segmentation