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Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting

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arxiv 2309.06824 v2 pith:XBPGP6ZA submitted 2023-09-13 cs.CV cs.AI

Xian Lin , Yangyang Xiang , Li Yu , Zengqiang Yan This is my paper · ORCID
classification cs.CVcs.AI
keywords segmentationimagesamusend-to-endultrasoundautosamusmedicalmodel
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End-to-end medical image segmentation is of great value for computer-aided diagnosis dominated by task-specific models, usually suffering from poor generalization. With recent breakthroughs brought by the segment anything model (SAM) for universal image segmentation, extensive efforts have been made to adapt SAM for medical imaging but still encounter two major issues: 1) severe performance degradation and limited generalization without proper adaptation, and 2) semi-automatic segmentation relying on accurate manual prompts for interaction. In this work, we propose SAMUS as a universal model tailored for ultrasound image segmentation and further enable it to work in an end-to-end manner denoted as AutoSAMUS. Specifically, in SAMUS, a parallel CNN branch is introduced to supplement local information through cross-branch attention, and a feature adapter and a position adapter are jointly used to adapt SAM from natural to ultrasound domains while reducing training complexity. AutoSAMUS is realized by introducing an auto prompt generator (APG) to replace the manual prompt encoder of SAMUS to automatically generate prompt embeddings. A comprehensive ultrasound dataset, comprising about 30k images and 69k masks and covering six object categories, is collected for verification. Extensive comparison experiments demonstrate the superiority of SAMUS and AutoSAMUS against the state-of-the-art task-specific and SAM-based foundation models. We believe the auto-prompted SAM-based model has the potential to become a new paradigm for end-to-end medical image segmentation and deserves more exploration. Code and data are available at https://github.com/xianlin7/SAMUS.

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Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FocalClick-XL: Towards Unified and High-quality Interactive Segmentation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    FocalClick-XL, a three-subnet extension of FocalClick, achieves state-of-the-art click-based interactive segmentation and supports boxes, scribbles, and coarse masks through a single prompting layer.

  2. SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation

    eess.IV 2025-04 conditional novelty 6.0 of 10

    RRL-MedSAM adapts SAM to one-shot 3D medical image segmentation through dual-stage knowledge distillation, mutual EMA weight updates, and an auto-prompting decoder, achieving state-of-the-art Dice on OASIS and CT-Lung.

  3. SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A multi-task segmentation model learns all, new, and vanishing MS lesions from heterogeneous MRI datasets using anatomical constraints and lesion-aware augmentation.

  4. EchoONE: Segmenting Multiple echocardiography Planes in One Model

    cs.CV 2024-12 conditional novelty 5.0 of 10

    EchoONE, a SAM-based model with prototype-composed dense prompts and a local-feature fusion branch, segments multiple echocardiography planes in one model and reports state-of-the-art Dice scores on internal and exter...

  5. Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction

    eess.IV 2024-11 conditional novelty 5.0 of 10

    OVS-Net, a SAM-based vessel segmentation framework with a micro-vessel enhancement branch and morphology-correction post-processing, reports higher Dice and better connectivity than six SAM baselines and 17 expert mod...

  6. Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    FA-SAM automates SAM-based medical segmentation across domains by generating prompt boxes with an uncertainty-enhanced network and fusing image and prompt embeddings.

  7. FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and Cup Segmentation in Fundus Images

    cs.CV 2025-02 conditional novelty 4.0 of 10

    FunduSAM, a SAM variant with adapters, CBAM, polar transformation, and a joint loss, reports top Dice/IoU for optic disc and cup segmentation on REFUGE.

  8. SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting

    cs.CV 2024-11 reject novelty 4.0 of 10

    A method that combines clustering, B-spline registration, and automatic prompting to adapt SAM for few-shot medical image segmentation with as few as 1 labeled image.

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