ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation

Challenge Description

Multiple Sclerosis (MS) is a neurological disease that affects millions of people worldwide. In MS, different area of the brain become inflamed, damaging the myelin, the fatty tissue that surrounds and insulate nerve fibers. Magnetic Resonance Imaging (MRI) is a fundamental tool to reach the diagnosis of MS and to monitor its progression and response to treatments. Accurate segmentation of MS lesions is essential for volumetric quantification of MS lesion load. However, manual selection of MS lesions on MRI scans is a very strenuous and time-consuming task and sufficiently large data sets with accurate manual segmentation by experts are still lacking. Consequently, the development of methods capable of automatically segmenting MS lesions is an unmet need and would represent a key step in advancing clinical management and optimizing treatment for people with MS. In conjunction with the ICPR 2024 conference, we propose a new lesion segmentation competition focused on improving the accuracy of MS lesion segmentation in MRI. We plan to provide participants with an extensively annotated dataset derived from a heterogeneous cohort of MS patients, which contains both baseline and follow-up MRI scan of each patient, acquired at different hospital. MSLesSeg focuses on developing algorithms that can independently segment MS lesions of an unexamined cohort of patients. This segmentation approach aims to overcome current benchmarks by eliminating user interaction and ensuring robust lesion detection at different timepoints, encouraging innovation and promoting methodological advances.

Main Contact

  • Name : Alessia Rondinella
  • Email :
  • Address : Dipartimento di Matematica e Informatica Cittadella Universitaria - Viale A. Doria 6 – Italy.


  • Alessia Rondinella, (Department of Mathematics and Computer Science, University of Catania, Italy)
  • Francesco Guarnera, (Department of Mathematics and Computer Science, University of Catania, Italy)
  • Sebastiano Battiato, (Department of Mathematics and Computer Science, University of Catania, Italy)
  • Elena Crispino, (Department of Biomedical and Biotechnological Sciences, University of Catania, Italy)
  • Giulia Russo, (Department of Drug and Health Sciences, University of Catania, Italy)
  • Francesco Pappalardo, (Department of Drug and Health Sciences, University of Catania, Italy)
  • Clara Di Lorenzo, (UOC Radiologia, Azienda Ospedaliera Garibaldi, Catania, Italy)
  • Davide Maiomone, (UOC Neurologia, Azienda Ospedaliera Cannizzaro, Catania, Italy)

Important dates

List of registered teams

Team NameOrganization/Institution
1MSUniCaTeamDept of Mathematics and Computer Science, University of Cagliari
2SJTU_SEIEE_2-426LabDepartment of Automation, Shanghai Jiao Tong University, Shanghai, China
3LSTLaboratory of Imaging Technologies, Faculty of Electrical Engineering, University of Ljubljana
4AthenaRC-DUTH Athena RC in conjuction with the Democritus University of Thrace
5UBHMUniversity of Bucharest, Helmholtz Munich
6UGIVIA-UIB TeamUniversity of the Balearic Islands
7MadSegUniversity of Wisconsin-Madison
8BrainSUniversity of Brescia
9NDSU REU ML North Dakota State University, USA
10VishleshakWorking Professional
11BeckLabIcahn School of Medicine at Mount Sinai, New York, NY, US.
12RVLabChungbuk National University/RVLab
13M3S: MeetMIALMSCIBM Center for Biomedical Imaging, Switzerland
14StudentUniversity of Cote d'Azur
15ARTEAMAltınay Robot Teknolojileri
16PicusLabMedUniversity of Naples Federico II
17ICAIUniversity of Malaga
18MMSTU Munich
19BronTeam John von Neumann Faculty of Informatics, Obuda University, Hungary
20AIMH4HealtcareISTI-CNR, Pisa, Italy
21INTELLIGO LabsUniversity of Verona
22LA2I2FUniversity of Udine
23Data ExplorersData Scientist Professional working in the Data Field
24TequilaGeorge Emil Palade University, Tirgu Mures, Romania
25GolestanGolestan university
26UMD-PRGUMD College Park
27Genshin Impact StartsHarbin Institute of Technology at Shenzhen
An email with a proper link for training set download will be sent to each teams
Training-set structure
  • Px (patient)
    • Tx (timpoint)
      • Px_Tx_T1.nii.gz (T1-weighted)
      • Px_Tx_T2.nii.gz (T2-weighted)
      • Px_Tx_FLAIR.nii.gz (FLAIR)
      • Px_Tx_MASK.nii.gz (Ground-Truth segmentation mask)
A link will be sent to the team email with a proper link for test set download
Test-set structure
  • Px (patient)
    • Px_T1.nii.gz (T1-weighted)
    • Px_T2.nii.gz (T2-weighted)
    • Px_FLAIR.nii.gz (FLAIR)
Upon completion of the challenge, participants are required to submit their results via email (from the registered email address) to the main contact ( The submission email should include a link to a repository with the following modality:
  • Subject: MSLesSeg Challenge Results TEAMNAME
  • The repository must contain a zip file to download named
  • The zip file must be organized as follow:
    • Directory DESC containing a PDF file with a brief summary of the segmentation approach used (1-2 pages)
    • Directory MASKS containing a list of files named Px_MASK.nii.gz.
    • 3D masks must have the same spatial resolutions (182,218,182) and orientations (X,Y,Z) of the Ground-Truth segmentation mask released during training phase
Only the last submission for team will be considered. Team with more than 3 submission will not be coonsidered
Competition at ICPR 2024

General info

Our initiative aims to fill existing gaps in fully automated MS lesion segmentation by providing participants with an extensively annotated MRI dataset derived from a heterogeneous cohort of MS patients. Our dataset contains series of data of about 90 patients acquired at multiple timepoints. These timepoint vary from 1 to 5 per patient. Thus, we have approximately 150 MRI data series (timepoints) in total. Each timepoint consists of three different scan modalities: T1-w, T2-w and FLAIR. This dataset was meticulously preprocessed and annotated on FLAIR sequences by our experts, encompassing T1-w and T2-w sequences for comprehensive lesion characterization. A notable strength of our initiative lies in the substantial number of patients and annotated scans, surpassing publicly available datasets traditionally used for MS lesion segmentation. Furthermore, our dataset is distinctively advantageous as it is representative of real-world scenarios, featuring authentic MS patients, and is acquired "in everyday practice", reflecting a heterogeneous acquisition environment without constraints. The goal is to build an algorithm capable of generating an automatic segmentation of MS lesions for an input MRI data series (T1-w, T2-w and FLAIR) not seen by the model under training. It is planned to provide a competition website/platform that will be used for the duration of the competition, which will remain active thereafter. The platform will contain a private area for participants; in this area participants can download the training/validation/test set, upload 1-2 page summary describing the methodology and results (segmentation masks obtained on the test set). In this competition, only fully automatic methods allowed. All methods may provide an automatic segmentation of the MS lesions. The data used in the training phase is limited to that provided by the challenge in order to compare methods in the same environment, so no additional data are allowed. Since this is the first challenge, it is not possible to estimate the exact number of participants. Given the recent interest in the context of multiple sclerosis lesion segmentation, we are confident that a significant number of groups will participate in the challenge (about 10). Furthermore, we plan to launch the first edition of the MSLesSeg competition at ICPR 2024, with subsequent editions planned as an annual tradition. We plan to ask each participant to produce a 1-2 page summary detailing their technical solutions to the challenge. Participants who do not comply with this request by the deadline will be excluded from the final rankings and awards. Teams with the highest scores in the challenge ranking will be reviewed by the organizing committee and will receive an electronic certificate of successful participation. The methods presented by these teams will also be described in the challenge report paper. The organizers of the challenge will provide the results of a baseline methods. The organizing committee will publish a paper describing the competition process and results (including rankings) of the challenge. The authors of the best selected works will be invited to submit their contribution to a special issue of a valuable Journal. Each participant may upload (during the upload time) a maximum of 3 result files; the best one will be considered for the participant's ranking. Each participant must upload also a 1-2 page summary describing the methodology before the deadline. After the deadline, the score will be calculated only for participant which upload the 1-2 page summary and at least one result file. All results will be shown publicly on the competition website, which will be updated during the open training round and announced on the final round phases of the closed test round of the task.


The dataset will be public. All the details about the dataset are available on the site used for the challenge. The dataset contains MRI scans of patients acquired at different timepoints and labelled scans and has received the approval of the ethics committee of the hospitals where the scans were acquired. The dataset and the annotations will be under CC BY 4.0 license. The competition cohort consists of patients diagnosed with multiple sclerosis who were clinically scanned with MRI brain acquisition protocols at different hospital centers in the city of Catania, Italy. All the data were acquired in the last 3 years, during brain MRI scans. The data were acquired in different hospital centers with different 1.5 Tesla MRI scanners (about 3), with different MRI protocols, then annotated by two expert raters and validated by an expert specialized in neurology and multiple sclerosis. The training, validation and test sets will be selected randomly from the dataset, to ensure that the distributions of multiple sclerosis patients conform to the distributions of a real-world scenario. All data provided during the challenge will be described on the challenge site. Each case consists of T1-w, T2-w and FLAIR-w scans (extracted from whole brain MRI) of a patient, acquired at several time-point, and annotations of the labelled MS lesions from the FLAIRs for each time-point. All the annotation were produced manually by two know-context operators and validated by a multiple sclerosis expert neurology. Both the annotators had professional experience on the task, while the neurologist which validated the segmentation masks had many years of experience in multiple sclerosis MRI evaluation. MS lesion annotations of the dataset were performed using Jim 9, a commercial semi-automated segmentation tool, the link to which can be found here: The process used to determine the annotations is as follows: 1. Each MRI was reviewed by an experienced neurologist specialising in multiple sclerosis. The neurologist identified and located the MS lesions using his expertise in MRI interpretation. 2. Two know-context operators segmented each identified lesion in the FLAIR scan, using T1- and T2- weighted scans for complete lesion characterisation. (MS lesions typically appear hyperintense in T2- weighted and FLAIR scans and hypointense in T1-weighted scans). The labels were generated using the tool mentioned above. 3. The neurologist verified the accuracy of the segmentations by visual inspection of the produced labels together with the corresponding MRI scans, manually correcting any discrepancies if necessary. Steps 2 and 3 were iterated until the neurologist was satisfied with the quality of the generated labels. During the manual labelling process, we consider a possible source of segmentation error resulting mainly from the existence of small MS lesions in MRI scans. Patients with multiple sclerosis often have numerous small lesions, usually less than 3 mm, so that the precise identification of these small lesions becomes difficult. To reduce potential inaccuracies, a validation process was implemented during the final data verification phase, systematically on all segmentation masks. Lesions that could not be distinctly identified on two consecutive slices were subsequently excluded from the segmentation masks. Errors in the segmentation process can also depend on inter- and intra-operator variability. To address this issue, the masks labeled by annotators underwent validation by the expert neurologist specializing in multiple sclerosis. We did an initial pre-processing on the MRIs: the original DICOM file was anonymised and converted to Nifti. It was then registered with the standard MNI-152 template. Finally, skull-stripping was performed, in order to extract only the brain tissue. All the annotations were produced after the registration of MRI with MNI-152 template from the FLAIR sequences, where the lesions appear hyperintense relative to the white matter. T1- and T2-weighted scans were used to confirm the presence of the lesions.


Participants will develop their methods using the training/validation dataset provided in the first phase. At the end of the first phase, they will evaluate the results of their method on the provided test set, which will be released in the second phase. Subsequently, participants will have to submit the results obtained on the test set (the predicted segmentation masks) and produce a 1-2 page summary describing their proposed method. The submission instruction will be available after the approval of the competition. Participants' methods will be evaluated based on their accuracy in segmenting MS lesions, measuring how well the predicted lesions from the proposed method overlap with the real lesions in the ground-truth mask, using a metric based on overlap. This overlap-based metric will be used to rank the proposed methods. The evaluation metric used will be the Mean Dice Score (DSC), which is a commonly used metric for evaluating the performance of binary segmentation. It is particularly useful in medical imaging for tasks such as lesion segmentation, where precise object delineation is critical. The Dice score value is between 0 and 1, where 0 means that there is no agreement between the predicted mask and the ground-truth mask, while a value of 1 means that the predicted mask and the ground-truth mask are completely overlapping. Specifically, we will calculate the Dice score for each segmentation mask generated by their proposed method (one mask for each data series in the test set) using the following formula:

where ‘A’ denotes the predicted segmentation mask and ‘B’ the ground-truth mask. Here, the notation denote the cardinality of each set of voxel. The final performance ranking will be determined by the overall Mean Dice Score calculated across all data series in the test set.


Alessia Rondinella


Department of Mathematics and Computer Science, University of Catania

Francesco Guarnera

Research Fellow

Department of Mathematics and Computer Science, University of Catania

Sebastiano Battiato

Full Professor

Department of Mathematics and Computer Science, University of Catania

Elena Crispino


Department of Biomedical and Biotechnological Sciences, University of Catania

Giulia Russo

Assistant Professor

Department of Drug and Health Sciences, University of Catani

Francesco Pappalardo

Full Professor

Department of Drug and Health Sciences, University of Catani

Clara Di Lorenzo

UOC Radiologia, Azienda Ospedaliera Garibaldi, Catania, Italy

Davide Maimone

UOC Radiologia, Azienda Ospedaliera Garibaldi, Catania, Italy


A. Rondinella, E. Crispino, F. Guarnera, O. Giudice, A. Ortis, G. Russo, C. Di Lorenzo, D. Maimone, F. Pappalardo, S. Battiato, “Boosting multiple sclerosis lesion segmentation through attention mechanism”. Computers in Biology and Medicine 161 (2023): 107021

A. Rondinella, F. Guarnera, O. Giudice, A. Ortis, G. Russo, E. Crispino, F. Pappalardo, S. Battiato “Enhancing Multiple Sclerosis Lesion Segmentation in Multimodal MRI Scans with Diffusion Models”. 2023 IEEE International Conference on Bioinformatics and Biomedicine Workshops (CMISF). IEEE, 2023