Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection

TL;DR

This paper introduces PDNet, a click-guided dual-path network that automatically segments lesions and predicts RECIST diameters in oncology images, reducing manual effort and improving accuracy.

Contribution

The paper proposes a novel prior-guided dual-path network with click-driven attention and multi-scale feature aggregation for lesion segmentation and diameter prediction.

Findings

PDNet outperforms existing methods on DeepLesion dataset.

It achieves higher accuracy in lesion segmentation.

It provides more precise RECIST diameter predictions.

Abstract

Measuring lesion size is an important step to assess tumor growth and monitor disease progression and therapy response in oncology image analysis. Although it is tedious and highly time-consuming, radiologists have to work on this task by using RECIST criteria (Response Evaluation Criteria In Solid Tumors) routinely and manually. Even though lesion segmentation may be the more accurate and clinically more valuable means, physicians can not manually segment lesions as now since much more heavy laboring will be required. In this paper, we present a prior-guided dual-path network (PDNet) to segment common types of lesions throughout the whole body and predict their RECIST diameters accurately and automatically. Similar to [1], a click guidance from radiologists is the only requirement. There are two key characteristics in PDNet: 1) Learning lesion-specific attention matrices in parallel from the click prior information by the proposed prior encoder, named click-driven attention; 2) Aggregating the extracted multi-scale features comprehensively by introducing top-down and bottom-up connections in the proposed decoder, named dual-path connection. Experiments show the superiority of our proposed PDNet in lesion segmentation and RECIST diameter prediction using the DeepLesion dataset and an external test set. PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.