SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI.
Year: 2018
Type: preprint
Abstract: Acute stroke lesion segmentation and prediction tasks are of great clinicalinterest as they can help doctors make better informed time-critical treatment
decisions. Automatic segmentation of these lesions is a complex task due to
their heterogeneous appearance, dynamic evolution and inter-patient
differences. Typically, acute stroke lesion tasks are approached with methods
developed for chronic stroke or other brain lesions. However, the
pathophysiology and anatomy of acute stroke establishes an inherently different
problem that needs special consideration. In this work, we propose a novel deep
learning architecture specially designed for acute stroke tasks that involve
approximating complex non-linear functions with reduced data. Within our
strategy, class imbalance is tackled using a hybrid strategy based on
state-of-the-art train sampling strategies designed for other brain lesion
related tasks, which is more suited to the anatomy and pathophysiology of acute
stroke lesions. The proposed method is evaluated on three unrelated public
international challenge datasets (ISLES) without any dataset specific
hyper-parameter tuning. These involve the tasks of sub-acute stroke lesion
segmentation, acute stroke penumbra estimation and chronic extent prediction
from acute MR images. The performance of the proposed architecture is analysed
both against similar deep learning architectures from chronic stroke and
related biomedical tasks and also by submitting the segmented test images for
blind online evaluation on each of the challenges. When compared with the rest
of submitted strategies, our method achieves top-rank performance among the
best submitted entries in all the three challenges, showing its capability to
deal with different unrelated tasks without hyper-parameter tuning. In order to
promote the reproducibility of our results, a public version of the proposed
method has been released.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2899013683