One Click Lesion RECIST Measurement and Segmentation on CT Scans

TL;DR

SEENet is a semi-automatic framework that enables rapid, accurate lesion segmentation and RECIST measurement on CT scans with minimal user input, improving efficiency and consistency in clinical trials.

Contribution

The paper introduces SEENet, a novel unified deep learning framework that automates lesion segmentation and RECIST estimation with only one click guidance, outperforming existing methods.

Findings

Achieves state-of-the-art performance on DeepLesion dataset.

Reduces radiologists' time and effort in lesion measurement.

Provides reliable lesion segmentation and RECIST estimation.

Abstract

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 \textit{SE}gmentation and RECIST \textit{E}stimation 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.