DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains

TL;DR

This paper introduces DBSegment, a deep learning-based method for fast, robust, and generalizable segmentation of deep brain structures from MRI, significantly reducing processing time compared to traditional registration-based methods.

Contribution

The paper presents DBSegment, a deep learning approach using nnU-Net that outperforms registration-based methods in speed and robustness, with high accuracy across diverse datasets.

Findings

Achieved an average DSC of 0.89 on independent datasets.

Reduced processing time from 42 minutes to 1 minute.

Demonstrated high generalizability across multiple domains.

Abstract

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject MRIs are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a robust and efficient deep brain segmentation solution. The method consists of a pre-processing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for independent testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and extensive testing on external datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average DSC of 0.89 $\pm$ 0.04 on the independent testing datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 42 minutes for a reference registration-based pipeline to 1 minute. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. The method is publicly available on GitHub, as well as a pip package for convenient usage.