# SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images

arXiv: https://arxiv.org/abs/2607.22332 · HTML: https://mlsanity.com/paper/2607.22332/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 74 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 29 of 29)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [Medical SAM 2: Segment medical images as video via Segment Anything Model 2](https://mlsanity.com/paper/2408.00874/) (2024)
- [SimpleClick: Interactive Image Segmentation with Simple Vision Transformers](https://mlsanity.com/paper/2210.11006/) (2022)
- [SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images](https://mlsanity.com/paper/2310.15161/) (2023)
- [MedSAM2: Segment Anything in 3D Medical Images and Videos](https://mlsanity.com/paper/2504.03600/) (2025)
- [AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking](https://mlsanity.com/paper/2407.16697/) (2024)
- [VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging](https://mlsanity.com/paper/2406.05285/) (2024)
- [TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI](https://mlsanity.com/paper/2405.19492/) (2024)
- [Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation](https://mlsanity.com/paper/2004.12537/) (2020)
- [CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography](https://mlsanity.com/paper/2105.14711/) (2021)
- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations](https://mlsanity.com/paper/2406.13844/) (2024)
- [Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI](https://mlsanity.com/paper/1903.07988/) (2019)
- [Lumbar spine segmentation in MR images: a dataset and a public benchmark](https://mlsanity.com/paper/2306.12217/) (2023)
- [Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry](https://mlsanity.com/paper/2108.10118/) (2021)
- [SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma](https://mlsanity.com/paper/2312.09576/) (2023)
- [Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge](https://mlsanity.com/paper/2408.12534/) (2024)
- [RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans](https://mlsanity.com/paper/2109.09521/) (2021)
- [SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging](https://mlsanity.com/paper/2402.17246/) (2024)
- [PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts](https://mlsanity.com/paper/2404.15028/) (2024)
- [NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale](https://mlsanity.com/paper/2107.05840/) (2021)
- [AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions](https://mlsanity.com/paper/2107.05451/) (2021)
- [AeroPath: An airway segmentation benchmark dataset with challenging pathology](https://mlsanity.com/paper/2311.01138/) (2023)
- [Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge](https://mlsanity.com/paper/2502.05330/) (2025)
- [Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results](https://mlsanity.com/paper/2505.08685/) (2025)
- [LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification](https://mlsanity.com/paper/2408.10069/) (2024)
- [MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset](https://mlsanity.com/paper/2503.13560/) (2025)

## Abstract (arXiv)

Interactive deep image segmentation enables efficient medical image annotation by iteratively refining predictions from user prompts, such as positive and negative clicks. Recent patch-based methods, including nnInteractive, achieve strong segmentation performance but remain limited in annotation workflows by high interaction latency, limited responsiveness to successive interactions, and the lack of support for reversible prompting. Furthermore, evaluation relies predominantly on simulated rather than controlled real-user interaction studies. We present SLIP, an end-to-end trainable framework for interactive 3D medical image segmentation that decouples image encoding from prompt-guided refinement. Image features are computed once and reused, while a lightweight patch memory bank maintains an interaction-aware segmentation state shared across patches. This representation enables prediction updates by propagating interaction context throughout the image, supports reversible prompting without recomputing image features, and substantially reduces interaction latency. By separating image representation from interactive reasoning, SLIP remains compatible with a wide range of image encoders. We train a single SLIP model for general interactive segmentation across diverse anatomical structures and imaging modalities. Beyond standard simulated evaluation, we conduct a controlled prospective user study comparing manual segmentation, nnInteractive, and SLIP across three clinical annotation tasks, six expert participants, and subjective usability measures, addressing the limited human validation of interactive segmentation methods. SLIP achieves SOTA interactive segmentation performance across 13 public datasets while providing lower interaction latency, greater responsiveness, support for reversible prompting, and higher user preference than existing approaches.