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CT-based automatic ...
CT-based automatic spine segmentation using patch-based deep learning
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- Qadri, Syed Furqan (författare)
- 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
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- Lin, Hongxiang (författare)
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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- Shen, Linlin (författare)
- AI Research Center for Medical Image Analysis and Diagnosis, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China
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- Ahmad, Mubashir (författare)
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Tobe Camp, Abbottabad, Pakistan
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- Qadri, Salman (författare)
- Computer Science Department, MNS-University of Agriculture, Multan, Pakistan
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- Khan, Salabat (författare)
- AI Research Center for Medical Image Analysis and Diagnosis, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China
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- Khan, Maqbool (författare)
- Software Competence Center Hagenberg GmbH, Softwarepark, Hagenberg, Linz, Austria; Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology, Mang, Haripur, Pakistan
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- Zareen, Syeda Shamaila (författare)
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
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- Akbar, Muhammad Azeem (författare)
- Lappeenranta University of Technology, Department of Information Technology, Lappeenranta, Finland
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- Bin Heyat, Md Belal (författare)
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China; Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Telangana, Hyderabad, India; Department of Science and Engineering, Novel Global Community Educational Foundation, NSW, Hebersham, Australia
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- Qamar, Saqib (författare)
- Umeå universitet,Institutionen för fysik
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(creator_code:org_t)
- Hindawi Publishing Corporation, 2023
- 2023
- Engelska.
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Ingår i: International Journal of Intelligent Systems. - : Hindawi Publishing Corporation. - 0884-8173 .- 1098-111X. ; 2023
- Relaterad länk:
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https://doi.org/10.1...
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https://umu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
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Qadri, Syed Furq ...
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Lin, Hongxiang
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Shen, Linlin
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Ahmad, Mubashir
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Qadri, Salman
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Khan, Salabat
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Khan, Maqbool
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Zareen, Syeda Sh ...
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Akbar, Muhammad ...
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Bin Heyat, Md Be ...
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Qamar, Saqib
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