Interactive 3D medical image segmentation methods typically require manual bounding box prompts, limiting their applicability in automated workflows. In this work, we propose an intensity-based thresholding strategy that automatically generates bounding box prompts when explicit annotations are unavailable. Our method leverages statistical properties of medical images to identify regions of interest through adaptive thresholding, morphological operations, and connected component analysis. Experiments on the CVPR BiomedSegFM dataset demonstrate that this automated prompting strategy significantly improves segmentation performance in high-contrast modalities, achieving 0.73 DSC for CT and 0.74 DSC for PET, compared to 0.68 and 0.59 respectively with standard prompts. However, the method faces challenges in low-contrast modalities such as Ultrasound, where performance decreases from 0.68 to 0.31 DSC due to speckle noise and ambiguous tissue boundaries. We also report preliminary experiments with lightweight MobileNet encoders as alternatives to Vision Transformers, finding that current lightweight architectures suffer substantial accuracy degradation in 3D medical segmentation tasks. Our results highlight both the promise and limitations of automated prompt generation for multi-modality medical imaging. Code: https://github.com/lexorcvpr/lexor-cvpr-2025/ .
3D biomedical image segmentation is a critical technology for clinical diagnostics, surgical planning, and disease analysis. While foundation models such as SAM and its medical derivatives have achieved remarkable progress, their adaptation to volumetric images remains limited, particularly in terms of diverse imaging modalities and the need for efficient user interaction. To advance research in this field, CVPR 2025 Interactive 3D Biomedical Image Segmentation Challenge was established. We propose DCM (DualClickMed) as a solution to this challenge, with a dual-expert architecture featuring both global and local Region-of-Interest (RoI) strategies. The global-RoI expert provides comprehensive anatomical context by processing the entire organ based on user prompts, while the local-RoI expert focuses on high-resolution patches centered on specific user clicks, enabling precise segmentation of fine structures. We further introduce tailored prompt simulation strategies for each expert, closely mimicking real-world interactive behaviors during training. Extensive experiments on challenge dataset covering five modalities demonstrate that our approach outperforms baselines, with final DSC scores of 0.8533 (CT), 0.6880 (MRI), 0.6003 (Microscopy), 0.7864 (PET), and 0.9385 (Ultrasound), achieving significant improvements in both region overlap and boundary accuracy metrics.
The increasing volume of complex 3D biomedical imaging data highlights the need for accurate and efficient analysis methods. Segmentation of such data is essential for diagnosis, anatomical analysis, disease monitoring, and treatment planning. However, existing segmentation algorithms often struggle with the variability of object structures and the diversity of imaging modalities. To address these challenges, we introduce iMedSTAM, a promptable foundation model for 3D image and video segmentation. The model is also capable of progressively improving segmentation quality based on user interactions. iMedSTAM was developed by fine-tuning EfficientTAM on a large-scale dataset comprising over 270,000 3D image–mask pairs and 4,000 video–mask pairs, covering five different medical imaging modalities. In addition, we extend the EfficientTAM architecture with a bidirectional inference and memory mechanism that enables the processing of volumetric data. iMedSTAM significantly outperforms all previous models on the publicly available validation set in the coreset track and achieves state-of-the-art results in the all-data track. On the test set, our model reaches an average final DSC and NSD of 0.805 and 0.842, respectively. For DSC_AUC and NSD_AUC, which measure the cumulative improvement through additional user interactions, iMedSTAM achieves scores of 3.129 and 3.258.
Existing 3D medical image segmentation methods are often constrained by a fixed set of predefined classes or by reliance on manually defined prompts such as bounding boxes and scribbles, which are often labor-intensive and prone to ambiguity. To address these limitations, we present a framework for 3D medical image segmentation across diverse modalities guided solely by free-text descriptions of target anatomies or diseases. Our solution is built on a multi-component architecture that integrates efficient feature encoding via decomposed 3D convolutions and self-attention, multi-scale text-visual alignment, and a SAM-inspired mask decoder with iterative refinement. The model is further conditioned through a prompt encoder that transforms language and intermediate visual cues into spatially aligned embeddings. To train and evaluate our model, we used a large-scale dataset of over 200,000 3D image-mask pairs spanning CT, MRI, PET, ultrasound, and microscopy. Our method achieved an average Dice of 0.6091 and F1_50 score of 0.1131 on the open validation set, outperforming baselines such as CAT (Dice 0.5316, F1_50 0.1935) and SAT (Dice 0.5573, F1_50 0.0956). It showed strong generalization across modalities, with particularly high performance on ultrasound (Dice 0.8337) and CT (Dice 0.6707). These results confirm the feasibility of free-text-guided 3D segmentation and establish our approach as a strong foundation model for general-purpose medical image segmentation. Our code is publicly available at: https://github.com/mirthAI/Text3DSAM/ .
Medical image segmentation is a critical task in clinical diagnostics and biomedical research. While deep learning has significantly advanced the field, most existing methods rely on task-specific models that require extensive manual annotations for training or adaptation. Vision foundation models, such as the Segment Anything Model (SAM), offer a promising alternative with their universal segmentation capabilities. However, their application to 3D medical imaging remains limited, especially in zero-shot scenarios involving previously unseen anatomical structures. In this work, we introduce GAMT, a zero-shot, training-free framework that repurposes powerful 2D foundation segmentation models (e.g., SAM, SAM-Med2D) for universal 3D biomedical image segmentation. To bridge the dimensionality gap, GAMT performs slice-wise inference along three orthogonal anatomical planes (axial, coronal, and sagittal) and subsequently fuses the predictions to construct a coherent 3D segmentation mask. Crucially, without any model training or fine-tuning, this framework achieves average Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) scores of 0.487 and 0.477, respectively—without requiring model training or fine-tuning. Our code and results are publicly available at https://github.com/SpatialAILab/GAMT.
