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Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

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arxiv 2304.04155 v1 pith:7HGAEUPU submitted 2023-04-09 eess.IV cs.CV

Ruining Deng , Can Cui , Quan Liu , Tianyuan Yao , Lucas W. Remedios , Shunxing Bao , Bennett A. Landman , Lee E. Wheless
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Lori A. Coburn Keith T. Wilson Yaohong Wang Shilin Zhao Agnes B. Fogo Haichun Yang Yucheng Tang Yuankai Huo
This is my paper · ORCID
classification eess.IVcs.CV
keywords segmentationmodelimageperformancezero-shotdigitalpathologyachieve
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The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation.

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Forward citations

Cited by 12 Pith papers

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

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    The KPIs 2024 challenge created a benchmark for glomerular segmentation in PAS-stained mouse kidney slides from four CKD models, and top models achieved Dice scores near 94 percent.

  3. Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation

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    Using a handful of labeled slices as memory in SAM2's video-segmentation pipeline enables prompt-free, fine-tuning-free segmentation of 3D medical volumes.

  4. Efficient Track Anything

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A lightweight video segmentation model with a vanilla ViT encoder and pooled memory cross-attention matches SAM 2 closely while running twice as fast and using 2.4x fewer parameters.

  5. Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Sli2Vol+ learns slice-to-slice correspondences with guidance from pseudo-labels, improving single-slice-annotated 3D segmentation over the Sli2Vol baseline by about 5.6 Dice points on CT and 3.5 on MRI.

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  7. Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine

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    An image-feature scoring engine plus k-means clustering selects prompt frames, improving SAM2-based segmentation in seven medical modalities.

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  12. Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation

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    Self-Prompt-SAM automatically generates point, box, and mask prompts for a fine-tuned SAM and reports state-of-the-art Dice scores on three medical segmentation benchmarks.

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