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Sökning: WFRF:(Toumpanakis Dimitrios) > (2022)

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1.
  • Banerjee, Subhashis, et al. (författare)
  • Topology-Aware Learning for Volumetric Cerebrovascular Segmentation
  • 2022
  • Ingår i: 2022 IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2022). - : IEEE. - 9781665429238 ; , s. 1-4
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a topology-aware learning strategy for volumetric segmentation of intracranial cerebrovascular structures. We propose a multi-task deep CNN along with a topology-aware loss function for this purpose. Along with the main task (i.e. segmentation), we train the model to learn two related auxiliary tasks viz. learning the distance transform for the voxels on the surface of the vascular tree and learning the vessel centerline. This provides additional regularization and allows the encoder to learn higher-level intermediate representations to boost the performance of the main task. We compare the proposed method with six state-of-the-art deep learning-based 3D vessel segmentation methods, by using a public Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) dataset. Experimental results demonstrate that the proposed method has the best performance in this particular context.
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2.
  • Pal, Subhash, et al. (författare)
  • Segmentation of Major Cerebral Vessel from MRA images and Evaluation using U-Net Family
  • 2022
  • 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
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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  • Resultat 1-2 av 2

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