Deep learning-based segmentation of various brain lesions for radiosurgery
Journal
Applied Sciences (Switzerland)
Journal Volume
11
Journal Issue
19
Date Issued
2021
Author(s)
Abstract
Featured Application: This study implemented deep learning methods to the task of segmentation of various brain lesions, facilitating the treatment planning process of neurosurgery and radiation oncology. Abstract: Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Brain tumors; Deep learning; Image segmentation; Magnetic resonance imaging; Radiosurgery
SDGs
Publisher
MDPI
Type
journal article