Lung Region Segmentation in Chest X-Ray Images using Deep Convolutional Neural Networks

Abstract

Lung cancer is, by far, the leading cause of cancer death in the world. Tools for automated medical imaging analysis development of a Computer-Aided Diagnosis method comprises several tasks. In general, the first one is the segmentation of region of interest, for example, lung region segmentation from Chest X-ray imaging in the task of detecting lung cancer. Deep Convolutional Neural Networks (DCNN) have shown promising results in the task of segmentation in medical images. In this paper, to implement the lung region segmentation task on chest X-ray images, was evaluated three different DCNN architectures in association with different regularization (Dropout, L2, and Dropout + L2) and optimization methods (SGDM, RMSPROP and ADAM). All networks were applied in the Japanese Society of Radiological Technology (JSRT) database. The best results were obtained using Dropout + L2 as regularization method and ADAM as optimization method. Considering the Jaccard Coefficient obtained (0.97967 ± 0.00232) the proposal outperforms the state of the art. Clinical Relevance-The presented method reduces the time that a professional takes to perform lung segmentation, improving the effectiveness.

Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: Real-world experience with a multicenter health screening cohort

“…Lunit INSIGHT CXR (Lunit) is a commercially available deep-learning algorithm-based CDSS for the automatic detection of thoracic abnormalities on chest X-ray (CXR). Recent studies have reported that AI systems using deep learning techniques can detect various diseases on CXRs, showing performance comparable to that of expert radiologists [ 3 – 9 ]. In previous studies, Lunit showed excellent diagnostic performance, which was similar to that of expert radiologists, and improved the performance of physicians in diagnosing pneumonia, lung cancer, tuberculosis, and multiple abnormal findings [ 6 , 10 , 11 ].…”

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

“…Deep learning models learn feature representation independently and utilize the learned highdimensional abstraction to finalize segmentation tasks without manual intervention (20). Recently, several studies have proposed various deep learning based automatic segmentation techniques for lung cancer (34)(35)(36)(37)(38)(39)(40)(41)(42). There is not yet a review of deep learning based automatic segmentation techniques for lung cancer radiotherapy.…”

A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image

“…Convolutional neural networks (CNNs) have brought about a revolution in the feld of DL, with their incredible success in various domains such as image classifcation [20], object detection [21], and image segmentation [22]. As a result, there has been a signifcant increase in interest from both academia and industry in recent years.…”