# Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images

arXiv: https://arxiv.org/abs/2311.14482 · HTML: https://mlsanity.com/paper/2311.14482/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images](https://mlsanity.com/paper/2203.12362/) (2022)
- [Interactive segmentation of medical images through fully convolutional neural networks](https://mlsanity.com/paper/1903.08205/) (2019)
- [iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images](https://mlsanity.com/paper/2112.11325/) (2021)
- [Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging](https://mlsanity.com/paper/2303.07126/) (2023)
- [DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images](https://mlsanity.com/paper/2305.10655/) (2023)
- [Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues](https://mlsanity.com/paper/2301.09914/) (2023)
- [AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier](https://mlsanity.com/paper/2209.01112/) (2022)
- [Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images](https://mlsanity.com/paper/2303.06942/) (2023)
- [PriorNet: lesion segmentation in PET-CT including prior tumor appearance information](https://mlsanity.com/paper/2210.02203/) (2022)

## Built on by (most cited first, 3 of 3)

- [Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods](https://mlsanity.com/paper/2404.01816/) (2024)
- [MOIS-SAM2: Exemplar-based Segment Anything Model 2 for multilesion interactive segmentation of neurofibromas in whole-body MRI](https://mlsanity.com/paper/2509.19277/) (2025)
- [Three-Phase Scribble-Adaptive Curriculum Learning for autoPETV Grand Challenge](https://mlsanity.com/paper/2608.22096/) (2026)

## Abstract (arXiv)

Deep learning has revolutionized the accurate segmentation of diseases in medical imaging. However, achieving such results requires training with numerous manual voxel annotations. This requirement presents a challenge for whole-body Positron Emission Tomography (PET) imaging, where lesions are scattered throughout the body. To tackle this problem, we introduce SW-FastEdit - an interactive segmentation framework that accelerates the labeling by utilizing only a few user clicks instead of voxelwise annotations. While prior interactive models crop or resize PET volumes due to memory constraints, we use the complete volume with our sliding window-based interactive scheme. Our model outperforms existing non-sliding window interactive models on the AutoPET dataset and generalizes to the previously unseen HECKTOR dataset. A user study revealed that annotators achieve high-quality predictions with only 10 click iterations and a low perceived NASA-TLX workload. Our framework is implemented using MONAI Label and is available: https://github.com/matt3o/AutoPET2-Submission/