Size‐adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset. Issue 11 (20th June 2022)

• Record Type: Journal Article
• Authors: Wang, Jun; Ji, Xiawei; Zhao, Mengmeng; Wen, Yaofeng; She, Yunlang; Deng, Jiajun; Chen, Chang; Qian, Dahong; Lu, Hongbing; Zhao, Deping
• Is Part Of: Medical physics. Volume 49:Issue 11(2022)
• Page Start: 7222
• Page End: 7236
• Publication Date: 2022-06-20
• Subjects: CAD system -- deep learning -- lesion detection -- mediastinal lesion; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15804
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these methods generally target one particular lesion type, that is, pulmonary nodules. In this work, we intend to develop and evaluate a novel deep learning method for a more challenging task, detecting various benign and malignant mediastinal lesions with wide variations in sizes, shapes, intensities, and locations in chest CT images. Methods: Our method for mediastinal lesion detection contains two main stages: (a) size‐adaptive lesion candidate detection followed by (b) false‐positive (FP) reduction and benign–malignant classification. For candidate detection, an anchor‐free and one‐stage detector, namely 3D‐CenterNet is designed to locate suspicious regions (i.e., candidates with various sizes) within the mediastinum. Then, a 3D‐SEResNet‐based classifier is used to differentiate FPs, benign lesions, and malignant lesions from the candidates. Results: We evaluate the proposed method by conducting five‐fold cross‐validation on a relatively large‐scale dataset, which consists of data collected on 1136 patients from a grade A tertiary hospital. The method can achieve sensitivity scores of 84.3% ± 1.9%, 90.2% ± 1.4%, 93.2% ± 0.8%, and 93.9% ± 1.1%, respectively, in finding all benign and malignant lesions at 1/8, 1/4, ½, and 1 FPs per scan, and the accuracy of benign–malignant classification can reach up to 78.7% ± 2.5%. Conclusions: The proposed method can effectively detect mediastinal lesions with various sizes, shapes, and locations in chest CT images. It can be integrated into most existing pulmonary lesion detection systems to promote their clinical applications. The method can also be readily extended to other similar 3D lesion detection tasks.