239 research outputs found
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
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
Lung Pattern Analysis using Artificial Intelligence for the Diagnosis Support of Interstitial Lung Diseases
Interstitial lung diseases (ILDs) is a group of more than 200 chronic lung disorders characterized by inflammation and scarring of the lung tissue that leads to respiratory failure. Although ILD is a heterogeneous group of histologically distinct diseases, most of them exhibit similar clinical presentations and their diagnosis often presents a diagnostic dilemma. Early diagnosis is crucial for making treatment decisions, while misdiagnosis may lead to life-threatening complications. If a final diagnosis cannot be reached with the high resolution computed tomography scan, additional invasive procedures are required (e.g. bronchoalveolar lavage, surgical biopsy). The aim of this PhD thesis was to investigate the components of a computational system that will assist radiologists with the diagnosis of ILDs, while avoiding the dangerous, expensive and time-consuming invasive biopsies. The appropriate interpretation of the available radiological data combined with clinical/biochemical information can provide a reliable diagnosis, able to improve the diagnostic accuracy of the radiologists.
In this thesis, we introduce two convolutional neural networks particularly designed for ILDs and a training scheme that employs knowledge transfer from the similar domain of general texture classification for performance enhancement. Moreover, we investigate the clinical relevance of breathing information for disease classification. The breathing information is quantified as a deformation field between inhale-exhale lung images using a novel 3D convolutional neural network for medical image registration. Finally, we design and evaluate the final end-to-end computational system for ILD classification using lung anatomy segmentation algorithms from the literature and the proposed ILD quantification neural networks. Deep learning approaches have been mostly investigated for all the aforementioned steps, while the results demonstrated their potential in analyzing lung images
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