# Slide-SAM: Medical SAM Meets Sliding Window

arXiv: https://arxiv.org/abs/2311.10121 · HTML: https://mlsanity.com/paper/2311.10121/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 37 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 26 of 26)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning](https://mlsanity.com/paper/1903.02351/) (2019)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation](https://mlsanity.com/paper/2306.13465/) (2023)
- ['Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images](https://mlsanity.com/paper/1902.01314/) (2019)
- [Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models](https://mlsanity.com/paper/2012.08721/) (2020)
- [SAM3D: Segment Anything Model in Volumetric Medical Images](https://mlsanity.com/paper/2309.03493/) (2023)
- [Differentiable Meta-learning Model for Few-shot Semantic Segmentation](https://mlsanity.com/paper/1911.10371/) (2019)
- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [Which images to label for few-shot medical landmark detection?](https://mlsanity.com/paper/2112.04386/) (2021)
- [Meta-Learning Initializations for Image Segmentation](https://mlsanity.com/paper/1912.06290/) (2019)

## Built on by (most cited first, 6 of 6)

- [Mobile U-ViT: Revisiting large kernel and U-shaped ViT for efficient medical image segmentation](https://mlsanity.com/paper/2508.01064/) (2025)
- [Deep Instruction Tuning for Segment Anything Model](https://mlsanity.com/paper/2404.00650/) (2024)
- [APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation](https://mlsanity.com/paper/2403.05114/) (2024)
- [Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations](https://mlsanity.com/paper/2403.15218/) (2024)
- [3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset](https://mlsanity.com/paper/2602.12820/) (2026)
- [Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation](https://mlsanity.com/paper/2603.17746/) (2026)

## Abstract (arXiv)

The Segment Anything Model (SAM) has achieved a notable success in two-dimensional image segmentation in natural images. However, the substantial gap between medical and natural images hinders its direct application to medical image segmentation tasks. Particularly in 3D medical images, SAM struggles to learn contextual relationships between slices, limiting its practical applicability. Moreover, applying 2D SAM to 3D images requires prompting the entire volume, which is time- and label-consuming. To address these problems, we propose Slide-SAM, which treats a stack of three adjacent slices as a prediction window. It firstly takes three slices from a 3D volume and point- or bounding box prompts on the central slice as inputs to predict segmentation masks for all three slices. Subsequently, the masks of the top and bottom slices are then used to generate new prompts for adjacent slices. Finally, step-wise prediction can be achieved by sliding the prediction window forward or backward through the entire volume. Our model is trained on multiple public and private medical datasets and demonstrates its effectiveness through extensive 3D segmetnation experiments, with the help of minimal prompts. Code is available at urlhttps://github.com/Curli-quan/Slide-SAM.