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Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks by Horry, Michael James; Chakraborty, Subrata; Pradhan, Biswajeet; Paul, Manoranjan; Zhu, Jing; Barua, Prabal Datta; Acharya, U. Rajendra; Chen, Fang; Zhou, Jianlong is a scholarly article available to read on EtoBox.

What is Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks about?

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more sub

Author

Horry, Michael James; Chakraborty, Subrata; Pradhan, Biswajeet; Paul, Manoranjan; Zhu, Jing; Barua, Prabal Datta; Acharya, U. Rajendra; Chen, Fang; Zhou, Jianlong

Published

2023

Language

EN