Ravi Soni

Sourya Sengupta, Satrajit Chakrabarty, Ravi Soni

The Segment Anything Model (SAM) has demonstrated impressive performance in zero-shot promptable segmentation on natural images. The recently released Segment Anything Model 2 (SAM 2) claims to outperform SAM on images and extends the model's capabilities to video segmentation. Evaluating the performance of this new model in medical image segmentation, specifically in a zero-shot promptable manner, is crucial. In this work, we conducted extensive studies using multiple datasets from various imaging modalities to compare the performance of SAM and SAM 2. We employed two point-prompt strategies: (i) multiple positive prompts where one prompt is placed near the centroid of the target structure, while the remaining prompts are randomly placed within the structure, and (ii) combined positive and negative prompts where one positive prompt is placed near the centroid of the target structure, and two negative prompts are positioned outside the structure, maximizing the distance from the positive prompt and from each other. The evaluation encompassed 24 unique organ-modality combinations, including abdominal structures, cardiac structures, fetal head images, skin lesions and polyp images across 11 publicly available MRI, CT, ultrasound, dermoscopy, and endoscopy datasets. Preliminary results based on 2D images indicate that while SAM 2 may perform slightly better in a few cases, it does not generally surpass SAM for medical image segmentation. Notably, SAM 2 performs worse than SAM in lower contrast imaging modalities, such as CT and ultrasound. However, for MRI images, SAM 2 performs on par with or better than SAM. Like SAM, SAM 2 also suffers from over-segmentation issues, particularly when the boundaries of the target organ are fuzzy.

Chen Liu, Xiaomeng Dong, Michael Potter et al.

Focal Loss has reached incredible popularity as it uses a simple technique to identify and utilize hard examples to achieve better performance on classification. However, this method does not easily generalize outside of classification tasks, such as in keypoint detection. In this paper, we propose a novel adaptation of Focal Loss for keypoint detection tasks, called Adversarial Focal Loss (AFL). AFL not only is semantically analogous to Focal loss, but also works as a plug-and-chug upgrade for arbitrary loss functions. While Focal Loss requires output from a classifier, AFL leverages a separate adversarial network to produce a difficulty score for each input. This difficulty score can then be used to dynamically prioritize learning on hard examples, even in absence of a classifier. In this work, we show AFL's effectiveness in enhancing existing methods in keypoint detection and verify its capability to re-weigh examples based on difficulty.

Satrajit Chakrabarty, Ravi Soni

Foundation models for promptable segmentation, including SAM, SAM 2, and the recently released SAM 3, have renewed interest in zero-shot segmentation of medical imaging. Although these models perform strongly on natural images, their behavior on medical data remains insufficiently characterized. While SAM 2 is widely used for annotation in 3D medical workflows, SAM 3 introduces a new perception backbone, detector-tracker pipeline, and concept-level prompting that may alter its behavior under spatial prompts. We present the first controlled comparison of SAM 2 and SAM 3 for zero-shot segmentation of 3D medical volumes and videos under purely visual prompting, with concept mechanisms disabled. We assess whether SAM 3 can serve as an out-of-the-box replacement for SAM 2 without customization. We benchmark both models on 16 public datasets (CT, MRI, 3D and cine ultrasound, endoscopy) covering 54 anatomical structures, pathologies, and surgical instruments. Prompts are restricted to the first frame and use four modes: single-click, multi-click, bounding box, and dense mask. This design standardizes preprocessing, prompt placement, propagation rules, and metric computation to disentangle prompt interpretation from propagation. Prompt-frame analysis shows that SAM 3 provides substantially stronger initialization than SAM 2 for click prompting across most structures. In full-volume analysis, SAM 3 retains this advantage for complex, vascular, and soft-tissue anatomies, emerging as the more versatile general-purpose segmenter. While SAM 2 remains competitive for compact, rigid organs under strong spatial guidance, it frequently fails on challenging targets where SAM 3 succeeds. Overall, our results suggest that SAM 3 is the superior default choice for most medical segmentation tasks, particularly those involving sparse user interaction or complex anatomical topology.

Sourya Sengupta, Satrajit Chakrabarty, Keerthi Sravan Ravi et al.

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 texture, contrast, and noise. Annotating medical images is costly and requires domain expertise, limiting large-scale annotated data availability. To address this, we propose SynthFM, a synthetic data generation framework that mimics the complexities of medical images, enabling foundation models to adapt without real medical data. Using SAM's pretrained encoder and training the decoder from scratch on SynthFM's dataset, we evaluated our method on 11 anatomical structures across 9 datasets (CT, MRI, and Ultrasound). SynthFM outperformed zero-shot baselines like SAM and MedSAM, achieving superior results under different prompt settings and on out-of-distribution datasets.