# One Click Lesion RECIST Measurement and Segmentation on CT Scans

arXiv: https://arxiv.org/abs/2007.11087 · HTML: https://mlsanity.com/paper/2007.11087/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography](https://mlsanity.com/paper/2406.05231/) (2024)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss](https://mlsanity.com/paper/2105.01218/) (2021)
- [Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection](https://mlsanity.com/paper/2105.01828/) (2021)
- [Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers](https://mlsanity.com/paper/2208.13113/) (2022)
- [Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation](https://mlsanity.com/paper/2307.15645/) (2023)
- [RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training](https://mlsanity.com/paper/2303.00205/) (2023)
- [AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation](https://mlsanity.com/paper/2202.13687/) (2022)
- [Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment](https://mlsanity.com/paper/2404.17235/) (2024)

## Abstract (arXiv)

In clinical trials, one of the radiologists' routine work is to measure tumor sizes on medical images using the RECIST criteria (Response Evaluation Criteria In Solid Tumors). However, manual measurement is tedious and subject to inter-observer variability. We propose a unified framework named SEENet for semi-automatic lesion textitSEgmentation and RECIST textitEstimation on a variety of lesions over the entire human body. The user is only required to provide simple guidance by clicking once near the lesion. SEENet consists of two main parts. The first one extracts the lesion of interest with the one-click guidance, roughly segments the lesion, and estimates its RECIST measurement. Based on the results of the first network, the second one refines the lesion segmentation and RECIST estimation. SEENet achieves state-of-the-art performance in lesion segmentation and RECIST estimation on the large-scale public DeepLesion dataset. It offers a practical tool for radiologists to generate reliable lesion measurements (i.e. segmentation mask and RECIST) with minimal human effort and greatly reduced time.