Semantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks.

Area of Science:

• Radiology
• Artificial Intelligence
• Medical Imaging

Background:

• Early and accurate diagnosis of interstitial lung diseases (ILDs) is critical for treatment but challenging due to similar manifestations of pathologies on thoracic CT scans.
• Radiologists face difficulties in distinguishing between various ILD patterns, necessitating advanced diagnostic tools.

Purpose of the Study:

• To propose a deep purely convolutional neural network (CNN) for the semantic segmentation of interstitial lung disease (ILD) patterns.
• To develop a core component for a computer-aided diagnosis system to aid in the identification of ILDs.

Main Methods:

• A deep purely convolutional neural network (CNN) with dilated filters was designed to process lung CT images of arbitrary sizes.
• The network was trained and validated on 172 sparsely annotated CT scans using a cross-validation approach.
• A semi-supervised learning strategy was employed, utilizing both labeled and unlabeled image regions for end-to-end training.

Main Results:

• The proposed CNN achieved significant performance improvements compared to existing state-of-the-art methods in segmenting ILD patterns.
• The semantic segmentation of ILD patterns demonstrated the effectiveness of the deep learning approach.

Conclusions:

• The developed deep convolutional neural network (CNN) shows promise as a foundational element for computer-aided diagnosis systems for interstitial lung diseases (ILDs).
• The approach offers a significant advancement in the automated analysis of thoracic CT scans for ILD detection and characterization.