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CT-based automatic spine segmentation using patch-based deep learning
Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China; AI Research Center for Medical Image Analysis and Diagnosis, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China.
Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
AI Research Center for Medical Image Analysis and Diagnosis, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China.
Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Tobe Camp, Abbottabad, Pakistan.
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2023 (English)In: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 2023, article id 2345835Article in journal (Refereed) Published
Abstract [en]

CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch-based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)-module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high-level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high-level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI-Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F-score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model's performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2023. Vol. 2023, article id 2345835
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Computer Sciences
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URN: urn:nbn:se:umu:diva-216893DOI: 10.1155/2023/2345835Scopus ID: 2-s2.0-85156094943OAI: oai:DiVA.org:umu-216893DiVA, id: diva2:1818133
Available from: 2023-12-08 Created: 2023-12-08 Last updated: 2023-12-08Bibliographically approved

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Qamar, Saqib

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