التفاصيل البيبلوغرافية
| العنوان: |
Acute and sub-acute stroke lesion segmentation from multimodal MRI |
| المؤلفون: |
Clèrigues, Albert, Oliver i Malagelada, Arnau, Lladó Bardera, Xavier, Valverde Valverde, Sergi, Bernal, Jose, Freixenet i Bosch, Jordi |
| المساهمون: |
Ministerio de Economía y Competitividad (Espanya) |
| المصدر: |
© Computer Methods and Programs in Biomedicine, 2020, vol. 194, art. núm. 105521 ; Articles publicats (D-ATC) |
| بيانات النشر: |
Elsevier |
| سنة النشر: |
2020 |
| المجموعة: |
Universitat de Girona: DUGiDocs (UdG Digital Repository) |
| مصطلحات موضوعية: |
Imatgeria per ressonància magnètica, Magnetic resonance imaging, Isquèmia cerebral -- Imatgeria per ressonància magnètica, Cerebral ischemia -- Magnetic resonance imaging, Imatgeria per al diagnòstic, Diagnostic imaging |
| الوصف: |
Background and objective. Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. Methods. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the sym- metry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a U-Net based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing. Results. The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Le- sion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC = 0.59 ±0.31) and SPES sub-tasks (DSC = 0.84 ±0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance. Conclusions. Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance. The same training ... |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/issn/0169-2607; info:eu-repo/semantics/altIdentifier/eissn/1872-7565; info:eu-repo/grantAgreement/MINECO//TIN2015-73563-JIN/ES/SEGMENTACION AUTOMATICA DE LAS ESTRUCTURAS CEREBRALES PARA SU USO COMO BIOMARCADORES DE IMAGEN/; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86696-R/ES/MODELOS PREDICTIVOS PARA LA ESCLEROSIS MULTIPE USANDO BIOMARCADORES DE RESONANCIA MAGNETICA DEL CEREBRO/; http://hdl.handle.net/10256/18453 |
| الاتاحة: |
http://hdl.handle.net/10256/18453 |
| Rights: |
Tots els drets reservats ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.722DEEDA |
| قاعدة البيانات: |
BASE |