CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images

Sepahvand, Nazanin Mohammadi; Arnold, Douglas L.

doi:10.1109/isbi45749.2020.9098554

Cited by 20 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sepahvand et al [ 78 ] used a convolutional neural network (CNN) to detect MS lesions using subtraction images on 1677 MRIs collected from 886 MS patients. For cross-validation, the training set was further divided into fivefold.…”

Section: Related Studiesmentioning

confidence: 99%

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Aslam

Khan

Bashamakh

et al. 2022

Sensors

View full text Add to dashboard Cite

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

show abstract

Section: Related Studiesmentioning

confidence: 99%

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Aslam

Khan

Bashamakh

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Sepahvand et al [199] introduced a U-Net based deep CNN classification method to detect New and Enlarging lesions (NE Lesions) of relapsing remitting MS. They improved the segmentation by subtracting images along with different time points then element-wise multiplying these images with the base references.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs

Shah¹,

S.²,

Ahmad³

et al. 2023

Preprint

View full text Add to dashboard Cite

Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain.Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs).Methodology: In this study, we review the different approaches that have been used in computer-aided detection and segmentation of MS lesions. Our review resulted in categorizing MS lesion segmentation approaches into six broad categories: data-driven, statistical, supervised machine learning, unsupervised machine learning, fuzzy, and deep learning-based techniques. We critically analyze the different techniques under these approaches and highlight their strengths and weaknesses.

show abstract

“…Automated change detection is a long-standing problem in medical imaging [6] and other fields [28,17]. Previous work on detecting change in longitudinal multiple sclerosis imaging include subtraction techniques to visualise areas of change [26], statistical modelling [34], and deep learning [5,24,19,35]. Approaches that jointly solve image registration and change detection have also been devised [7,11].…”

Section: Related Workmentioning

confidence: 99%

Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging

To¹,

Sarno²,

Chong³

et al. 2021

Preprint

View full text Add to dashboard Cite

Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant impact on clinical decision making, highlighting the important role for automated change detection. Lesion changes can represent anomalies in serial imaging, which implies a limited availability of annotations and a wide variety of possible changes that need to be considered. Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any lesion changes.Our training automatically synthesises lesion changes in serial images, introducing detection and localisation pseudo-labels that are used to self-supervise the training of our model. Given the rarity of these lesion changes in the synthesised images, we train the model with the imbalance robust focal Tversky loss. When compared to supervised models trained on different datasets, our method shows competitive performance in the detection and localisation of new demyelinating lesions on longitudinal magnetic resonance imaging in multiple sclerosis patients. Code for the models will be made available on GitHub.

show abstract

CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images

Cited by 20 publications

References 10 publications

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs

Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging

Contact Info

Product

Resources

About