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Segmentation of Maj...
Segmentation of Major Cerebral Vessel from MRA images and Evaluation using U-Net Family
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- Pal, Subhash (författare)
- National Institute of Technology, Durgapur, India
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- Banerjee, Subhashis (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion
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- Toumpanakis, Dimitrios (författare)
- Uppsala universitet,Radiologi
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- Wikström, Johan, Professor, 1964- (författare)
- Uppsala universitet,Radiologi
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- Strand, Robin, 1978- (författare)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
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- Dhara, Ashis (författare)
- National Institute of Technology, Durgapur, India
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
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Ingår i: 2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665473804 ; , s. 235-238
- Relaterad länk:
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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
- Arterial cerebral vessel assessment is critical for thediagnosis of patients with cerebrovascular disease e.g., hypertension, Intracranial aneurysms, and dementia. Magnetic resonance angiography is a primary imaging technique for diagnosing cerebrovascular diseases. There are many Convolutional neuralnetworks (CNN) based methods for cerebral vessel segmentation but lack to identify the target vessels and understand the arterial tree structure for diagnosis and endovascular surgical planning.In the present study, we generated annotations for major vesselsegmentation and analyzed fully automatic segmentation of major vessels using state-of-the-art U-Net based deep learning models. Computer-aided major cerebral vessel segmentation incorporatedinto clinical practice may help speed up the diagnosis of time-critical vessel anomalies and help find important bio-markers for neurological dysfunction. We validated and compared U-Net based models for volumetric segmentation and predictionof cerebral arteries and it could be done in real-time withoutany image pre-processing.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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