Multiple Sclerosis Spinal Cord Lesions Detection from MultiSequence MRIs Challenge (MS-Multi-Spine)
Creators: name: Casey, Romain
name: Combès, Benoit
name: Cotton, François
name: Dojat, Michel
name: Kain, Michael
name: Kerbrat, Anne
name: Pop, Sorina
Publisher: name: Zenodo
Year: 2024
Subjects: Multiple Sclerosis
Spinal Cord
MRI
Lesion
Instance Segmentation
Detection
Missing Modalities
Missing Data
MICCAI 2025 challenge

description: Multiple Sclerosis (MS) is a common and potentially debilitating disease affecting around 3 million persons in the world. Currently, Magnetic Resonance Imaging (MRI) plays a central role in this context and in particular allows the identification of MS lesions in the central nervous system. The identification of these lesions on a given MRI image is a complex and mentally demanding task that often leads to an underestimation of disease activity, even for most experienced radiologists. There is thus a need for automated tools that can provide clinicians an aid for accurate and robust identification and quantification of MS lesions. To date, the medical imaging community concentrated its efforts toward the segmentation of the lesions in brain MRI. For this purpose, over the past years, several challenges have been organized to assess the ability of automated methods to detect multiple sclerosis (MS) lesions as compared to manual delineation (The longitudinal lesion challenge; https://smart-stats-tools.org/lesion-challenge, MSSeg: https://portal.fli-iam.irisa.fr/msseg-challenge/, MSSeg2 https://portal.fli-iam.irisa.fr/msseg-2/). These have allowed the community to explore innovative directions. The proposed MS-Multi-Spine challenge aims at offering the possibility to the medical imaging community to extend their methods to spinal cord lesions. This is an innovative challenge both from a clinical and methodological perspective.

1) Clinically, the presence of lesions in the spinal cord has a major prognostic value compared to brain lesions [1]. However, in clinical practice their detection represents a hard task for radiologists. Indeed, MS lesion detection/segmentation in spinal cord MRI is a complex task due to specific characteristics:



the size of the anatomical structures of interest (around 1cm diameter) resulting in high occurrence of partial volume effects and thus less sharp gradients and contrasts between distinct normal appearing and pathological tissues;

the occurrence of significant artifacts due to subjects motion and respiration.


As a result, despite its clinical importance, spinal cord MRI is currently under-exploited in patients with MS.Providing clinicians with tools capable of reliably identifying these spinal cord lesions would therefore be a major added-value.

2) Methodologically, spinal cord lesion detection raises a specific challenge. Indeed, in clinical practice, it is highly recommended to acquire at least two sequences among a set of available sequences, without specific guidelines to date. In practice, depending on the center and context, any combination of existing MR sequences can be provided. In this challenge, that represents a concrete complex case of multisequence datasets, we focus on four commonly used sequences: the sagittal T2 (that is always provided in the challenge and will be considered as the reference to segment), the sagittal STIR, the sagittal PSIR and the 3D MP2RAGE. From a methodological point ofview, this is a concrete and paradigmatic case of missing modalities setting where, depending on the case, some modalities may be missing both at inference or training time. To the best of our knowledge, such clinical datasets are still rarely available in medical imaging.

[1] Early imaging predictors of long-term outcomes in relapse-onset multiple sclerosis Brownlee WJ, Altmann DR, Prados F, Miszkiel KA, Eshaghi A et al. Brain, 2019[2] 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis Wattjes MP, Ciccarelli O, Reich DS, Banwell B, de Stefano N, Enzinger C et al. Lancet Neurol., 2021