Pneumothorax detection and segmentation from chest X-ray radiographs using a patch-based fully convolutional encoder-decoder network

Pneumothorax, a life-threatening condition characterized by air accumulation in the pleural cavity, requires early and accurate detection for optimal patient outcomes. Chest X-ray radiographs are a common diagnostic tool due to their speed and affordability. However, detecting pneumothorax can be ch...

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Vydané v:	Frontiers in radiology Ročník 4; s. 1424065
Hlavní autori:	Dumbrique, Jakov Ivan S., Hernandez, Reynan B., Cruz, Juan Miguel L., Pagdanganan, Ryan M., Naval, Prospero C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Media S.A 11.12.2024
Predmet:	automatic image segmentation convolutional neural network deep learning lung pathology detection pneumothorax Radiology Vision Transformer chest X-rays deep learning lung pathology detection automatic image segmentation convolutional neural network Vision Transformer diagnostic radiology pneumothorax
ISSN:	2673-8740, 2673-8740
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Shrnutí:	Pneumothorax, a life-threatening condition characterized by air accumulation in the pleural cavity, requires early and accurate detection for optimal patient outcomes. Chest X-ray radiographs are a common diagnostic tool due to their speed and affordability. However, detecting pneumothorax can be challenging for radiologists because the sole visual indicator is often a thin displaced pleural line. This research explores deep learning techniques to automate and improve the detection and segmentation of pneumothorax from chest X-ray radiographs. We propose a novel architecture that combines the advantages of fully convolutional neural networks (FCNNs) and Vision Transformers (ViTs) while using only convolutional modules to avoid the quadratic complexity of ViT’s self-attention mechanism. This architecture utilizes a patch-based encoder-decoder structure with skip connections to effectively combine high-level and low-level features. Compared to prior research and baseline FCNNs, our model demonstrates significantly higher accuracy in detection and segmentation while maintaining computational efficiency. This is evident on two datasets: (1) the SIIM-ACR Pneumothorax Segmentation dataset and (2) a novel dataset we curated from The Medical City, a private hospital in the Philippines. Ablation studies further reveal that using a mixed Tversky and Focal loss function significantly improves performance compared to using solely the Tversky loss. Our findings suggest our model has the potential to improve diagnostic accuracy and efficiency in pneumothorax detection, potentially aiding radiologists in clinical settings.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Curtise K. C. Ng, Curtin University, Australia Reviewed by: Yanfu Zhang, University of Pittsburgh, United States Syoji Kobashi, University of Hyogo, Japan
ISSN:	2673-8740 2673-8740
DOI:	10.3389/fradi.2024.1424065