5 papers
Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data
Satrajit Chakrabarty, Ravi Soni
Foundation models, such as the Segment Anything Model (SAM), have heightened interest in promptable zero-shot segmentation. Although these models perform strongly on natural images…
Synthetic Volumetric Data Generation Enables Zero-Shot Generalization of Foundation Models in 3D Medical Image Segmentation
Satrajit Chakrabarty, Sourya Sengupta, Gopal Avinash +1
Foundation models such as Segment Anything Model 2 (SAM 2) exhibit strong generalization on natural images and videos but perform poorly on medical data due to differences in appea…
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI
Nazanin Maleki, Raisa Amiruddin, Ahmed W. Moawad +240
Despite continuous advancements in cancer treatment, brain metastatic disease remains a significant complication of primary cancer and is associated with an unfavorable prognosis.…
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
Peijie Qiu, Satrajit Chakrabarty, Phuc Nguyen +2
Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tool…
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data
Sourya Sengupta, Satrajit Chakrabarty, Keerthi Sravan Ravi +2
Foundation models like the Segment Anything Model (SAM) excel in zero-shot segmentation for natural images but struggle with medical image segmentation due to differences in textur…