LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation

Abstract

Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced a lesion segmentation tool, LST, engineered with a lesion growth algorithm (LST-LGA). While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. Here, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D-UNets.LST-AI specifically addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1w and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI additionally includes a lesion location annotation tool, labeling lesion location according to the 2017 McDonald criteria (periventricular, infratentorial, juxtacortical, subcortical). We conduct evaluations on 270 test cases —comprising both in-house (n=167) and publicly available data (n=103)—using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models.Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.5, outperforming LST-LGA, LST-LPA, SAMSEG, and the popular nnUNet framework, which all scored below 0.45. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63—surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions larger than 60mm3.Given its higher segmentation performance, we recommend that research groups currently using LST-LGA transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.

Longitudinal evidence for a mutually reinforcing relationship between white matter hyperintensities and cortical thickness in cognitively unimpaired older adults

“…We manually segmented WMH using the AI-augmented version of the Lesion Segmentation Toolbox (LST-AI) [42][43][44] and based the segmentation on both T1w MPRAGE and T2w FLAIR imaging data. We then tallied WMH volumes across the frontal, temporal, parietal and occipital lobes using the UCSLobes Atlas [45].…”