Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery

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.

Automated summary

This study evaluated deep learning segmentation algorithms for medical image analysis, particularly in the context of brain lesions. By benchmarking state-of-the-art models on a clinical dataset, the study identified strengths, weaknesses, and potential improvements for deep learning algorithms in assisting with brain lesion segmentation. The results suggest that deep learning shows promise in this application, even with high heterogeneity in lesion types and sizes within the training dataset.