التفاصيل البيبلوغرافية
| العنوان: |
Minimizing the effect of white matter lesions on deep learning based tissue segmentation for brain volumetry |
| المؤلفون: |
Clèrigues, Albert, Valverde Valverde, Sergi, Salvi, Joaquim, Oliver i Malagelada, Arnau, Lladó Bardera, Xavier |
| المساهمون: |
Agencia Estatal de Investigación |
| المصدر: |
Computerized Medical Imaging and Graphics, 2023, vol. 103, art.núm. 102157 ; Articles publicats (D-ATC) |
| بيانات النشر: |
Elsevier |
| سنة النشر: |
2023 |
| المجموعة: |
Universitat de Girona: DUGiDocs (UdG Digital Repository) |
| مصطلحات موضوعية: |
Imatgeria per ressonància magnètica, Magnetic resonance imaging, Desmielinització -- Imatgeria per ressonància magnètica, Demyelination -- Magnetic resonance imaging, Imatgeria tridimensional en medicina, Three-dimensional imaging in medicine, Imatges -- Segmentació, Image segmentation, Visió per ordinador en medicina, Computer vision in medicine |
| الوصف: |
Automated methods for segmentation-based brain volumetry may be confounded by the presence of white matter (WM) lesions, which introduce abnormal intensities that can alter the classification of not only neighboring but also distant brain tissue. These lesions are common in pathologies where brain volumetry is also an important prognostic marker, such as in multiple sclerosis (MS), and thus reducing their effects is critical for improving volumetric accuracy and reliability. In this work, we analyze the effect of WM lesions on deep learning based brain tissue segmentation methods for brain volumetry and introduce techniques to reduce the error these lesions produce on the measured volumes. We propose a 3D patch-based deep learning framework for brain tissue segmentation which is trained on the outputs of a reference classical method. To deal more robustly with pathological cases having WM lesions, we use a combination of small patches and a percentile-based input normalization. To minimize the effect of WM lesions, we also propose a multi-task double U-Net architecture performing end-to-end inpainting and segmentation, along with a training data generation procedure. In the evaluation, we first analyze the error introduced by artificial WM lesions on our framework as well as in the reference segmentation method without the use of lesion inpainting techniques. To the best of our knowledge, this is the first analysis of WM lesion effect on a deep learning based tissue segmentation approach for brain volumetry. The proposed framework shows a significantly smaller and more localized error introduced by WM lesions than the reference segmentation method, that displays much larger global differences. We also evaluated the proposed lesion effect minimization technique by comparing the measured volumes before and after introducing artificial WM lesions to healthy images. The proposed approach performing end-to-end inpainting and segmentation effectively reduces the error introduced by small and large WM lesions in the ... |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/issn/0895-6111; info:eu-repo/semantics/altIdentifier/eissn/1879-0771; DPI2020-114769RB-I00; http://hdl.handle.net/10256/22460 |
| الاتاحة: |
http://hdl.handle.net/10256/22460 |
| Rights: |
Attribution-NonCommercial-NoDerivatives 4.0 International ; http://creativecommons.org/licenses/by-nc-nd/4.0/ ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.E8147617 |
| قاعدة البيانات: |
BASE |