التفاصيل البيبلوغرافية
| العنوان: |
Intracranial Hemorrhage Segmentation Using A Deep Convolutional Model |
| المؤلفون: |
Murtadha D. Hssayeni, Muayad S. Croock, Aymen D. Salman, Hassan Falah Al-khafaji, Zakaria A. Yahya, Behnaz Ghoraani |
| المصدر: |
Data, Vol 5, Iss 1, p 14 (2020) |
| بيانات النشر: |
MDPI AG, 2020. |
| سنة النشر: |
2020 |
| المجموعة: |
LCC:Bibliography. Library science. Information resources |
| مصطلحات موضوعية: |
intracranial hemorrhage segmentation, ich detection, fully convolutional network, u-net, ct scans dataset, Bibliography. Library science. Information resources |
| الوصف: |
Traumatic brain injuries may cause intracranial hemorrhages (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with a traumatic brain injury. Next, the ICH regions were manually delineated in each slice by a consensus decision of two radiologists. The dataset is publicly available online at the PhysioNet repository for future analysis and comparisons. In addition to publishing the dataset, which is the main purpose of this manuscript, we implemented a deep Fully Convolutional Networks (FCNs), known as U-Net, to segment the ICH regions from the CT scans in a fully-automated manner. The method as a proof of concept achieved a Dice coefficient of 0.31 for the ICH segmentation based on 5-fold cross-validation. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2306-5729 |
| Relation: |
https://www.mdpi.com/2306-5729/5/1/14; https://doaj.org/toc/2306-5729 |
| DOI: |
10.3390/data5010014 |
| URL الوصول: |
https://doaj.org/article/cc7eea96d5b945ccb9db242e369376e5 |
| رقم الانضمام: |
edsdoj.7eea96d5b945ccb9db242e369376e5 |
| قاعدة البيانات: |
Directory of Open Access Journals |