Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks

Abstract

Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnostic procedure is based on the detection and recognition of the different ILD pathologies in thoracic CT scans, yet their manifestation often appears similar. In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs. The proposed CNN, which consists of convolutional layers with dilated filters, takes as input a lung CT image of arbitrary size and outputs the corresponding label map. We trained and tested the network on a dataset of 172 sparsely annotated CT scans, within a cross-validation scheme. The training was performed in an end-to-end and semi-supervised fashion, utilizing both labeled and non-labeled image regions. The experimental results show significant performance improvement with respect to the state of the art.

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
• ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
• Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
• Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
• Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis
• Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling

Cited by in corpus (7)

• Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
• Deep Learning in Multi-organ Segmentation
• Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung Infections in Chest CT volumes based on Emergent Languages
• An Abstraction Model for Semantic Segmentation Algorithms
• Segmentation of Lung Tumor from CT Images using Deep Supervision
• CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation
• Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images