2026-04-13T05:04:47Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/579452025-12-21T20:46:52Zcom_2072_6col_2072_452954
RECERCAT
author
Gambús i Moreno, Paula
2023-09-22T17:01:38Z2023-09-22T17:01:38Z2023-09-22
Tutors: Dr. Deborah Pareto Onghena, Dr. Gerard Martí Juan.
Treball de fi de grau en BiomèdicaMultiple sclerosis (MS) is a neurodegenerative disease affecting the central nervous
system (CNS), characterized by the destruction of myelin sheaths, that has become
the leading cause of disability in young adults. Since this disease does not have a
cure, an early diagnosis is crucial to start treatment and slow down its progression.
Current diagnostic criteria are based on detecting lesions on magnetic resonance
imaging (MRI). Accurate detection of spinal cord (SC) lesions is currently missing.
In this context, the aim of this project was to develop an artificial intelligence
tool using convolutional neural networks to automatically segment SC lesions from
Double Inversion Recovery (DIR) sequences.
For this purpose, two different raters manually segmented SC lesions from patients
with MS. From these masks, three different networks were obtained using the
same hyperparameters: the Rater 1 model was trained with masks segmented by
the first rater; the Rater 2 model with segmentations from the rater 2; and the
Hybrid one, with masks from both of them. Their performance was evaluated using
a test set of 30 patients, with half of them not having SC lesions. To assess the
performance of the method, the Dice coefficient between the manual ground truths
and the automatic masks was calculated.
The three models showed good performance, with a Dice score of around 50%.
However, the Rater 2 model had better results since a Dice coefficient of 0.557
± 0.102 was obtained. Moreover, this algorithm demonstrated a non-overlapping
volume percentage of 34.119 ± 49.629 % meaning that it exhibited a tendency to
over-segment lesions, leading to false positives. Further analysis and discussions with
neuroradiologists determined that removing falsely detected voxels was preferable
to missing true SC lesions.
Even though the successful results, further improvements to increase its accuracy
are necessary for clinical viability. Overall, this research presents a step towards
automated SC lesion segmentation in MS using DIR sequences, which could aid to
assist radiologists in scanner assessment.
Llicència CC Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/deed.ca info:eu-repo/semantics/openAccess
Multiple sclerosis
Spinal cord lesion
Deep learning
Convolutional neural network
DIR sequence
Ground truth
Dice coefficient
Automatic segmentation of intramedullary multiple sclerosis lesions delimited in DIR sequences with convolutional neural networks
info:eu-repo/semantics/bachelorThesis