Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods

Zhang, Huahong; Oguz, Ipek

doi:10.1007/978-3-030-72084-1_2

Cited by 13 publications

(9 citation statements)

References 80 publications

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“…DLM train and learn themselves to design features directly from data [6] and provide best results in several problems, including the case of MS lesion identification/segmentation [4,27,33,37,71,76]. This has also been confirmed in recent reviews [19,21,37,75].…”

Section: Related Workmentioning

confidence: 94%

“…Medical image analysis is greatly performed with automated methods, mostly involving deep learning [42]. Automated MS lesion identification/segmentation is still an active field of research and several methods have been provided in the last decade and well reviewed along time [19,21,23,37,43,46,75] and the role of AI-based methods is emerging [2]. Automated strategies can be classified in three main groups: methods using pre-selected features (PSFM), methods using a-priori information (APIM) and methods using deep learning (DLM).…”

Section: Related Workmentioning

confidence: 99%

“…Despite that, residual uncertainty remains due to PVE, to the unspecific role of MRI for MS and to MS intrinsic variability. Specific automatic frameworks have been recently proposed and reviewed [4,19,21,26,27,37,75] also for evaluating MS temporal progression [25,52,59]. However, to date the results are still far from those of human experts, though the efforts trough the increasing of model/architectural complexity.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble CNN and Uncertainty Modeling to Improve Automatic Identification/Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Imaging

Placidi¹,

Cinque²,

Iacoviello³

et al. 2021

Preprint

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To date, several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions by Magnetic Resonance Imaging (MRI) have been presented which are either outperformed by human experts or, at least, whose results are well distinguishable from humans. This is due to the ambiguity originated by MRI instabilities, peculiar MS Heterogeneity and MRI unspecific nature with respect to MS. Physicians partially treat the uncertainty generated by ambiguity relying on personal radiological/clinical/anatomical background and experience.We present an automated framework for MS lesions identification/segmentation based on three pivotal concepts to better emulate human reasoning: the modeling of uncertainty; the proposal of two, separately trained, CNN, one optimized with respect to lesions themselves and the other to the environment surrounding lesions, respectively repeated for axial, coronal and sagittal directions; the ensemble of the CNN output.The proposed framework is trained, validated and tested on the 2016 MSSEG benchmark public data set from a single imaging modality, FLuid-Attenuated Inversion Recovery (FLAIR). The comparison, performed on the segmented lesions by means of most of the metrics normally used with respect to the ground-truth and the 7 human raters in MSSEG, prove that there is no significant difference between the proposed framework and the other raters. Results are also shown for the uncertainty, though a comparison with the other raters is impossible.

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Section: Related Workmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble CNN and Uncertainty Modeling to Improve Automatic Identification/Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Imaging

Placidi¹,

Cinque²,

Iacoviello³

et al. 2021

Preprint

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“…Thus, brain extraction followed by N4 bias field correction (Tustison et al, 2010 ) was performed on these raw 3D images using the Anima MS longitudinal preprocessing script 3 . Intensity normalization was performed on each 3D MRI scan using the 99 th percentile and Kernel Density Estimate (KDE) with the Gaussian kernel similar to one described by Reinhold et al ( 2019 ) and Zhang and Oguz ( 2020 ). Then, early fusion was performed on the baseline and follow-up images to produce 2-channel input data allowing the proposed model to obtain temporal features from MRI sequences.…”

Section: Methodsmentioning

confidence: 99%

New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images

Sarica

Şeker²

2022

Front. Neurosci.

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Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units which facilitate the training of deep networks. Networks with fewer parameters are designed with better performance through the skip connections of U-Net and residual units, which facilitate information propagation without degradation. Attention gates also learn to focus on salient features of the target structures of various sizes and shapes. The MSSEG-2 dataset was used for training and testing the proposed pipeline, and the results were compared with those of other proposed pipelines of the challenge and experts who participated in the same challenge. According to the results over the testing set, the lesion-wise F1 and dice scores were obtained as a mean of 48 and 44.30%. For the no-lesion cases, the number of tested and volume of tested lesions were obtained as a mean of 0.148 and 1.488, respectively. The proposed pipeline outperformed 22 proposed pipelines and ranked 8th in the challenge.

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“…There are multiple ways for MS lesion detection, and some of them are (i) intensity-based approaches, which depend on detecting the changes of intensity ( 7 , 8 ), (ii) deformation-based approaches, which analyse the deformation of brain tissue ( 9 , 10 ), (iii) segmentation-based approaches, which segment white matter hyper-intensities from the acquired scans ( 11 , 12 ), and (iv) subtraction-based approaches, which depend on subtracting two longitudinal scans ( 7 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions

et al. 2022

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Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system that affects nearly 1 million adults in the United States. Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis and treatment monitoring in MS patients. In particular, follow-up MRI with T2-FLAIR images of the brain, depicting white matter lesions, is the mainstay for monitoring disease activity and making treatment decisions. In this article, we present a computational approach that has been deployed and integrated into a real-world routine clinical workflow, focusing on two tasks: (a) detecting new disease activity in MS patients, and (b) determining the necessity for injecting Gadolinium Based Contract Agents (GBCAs). This computer-aided detection (CAD) software has been utilized for the former task on more than 19, 000 patients over the course of 10 years, while its added function of identifying patients who need GBCA injection, has been operative for the past 3 years, with > 85% sensitivity. The benefits of this approach are summarized in: (1) offering a reproducible and accurate clinical assessment of MS lesion patients, (2) reducing the adverse effects of GBCAs (and the deposition of GBCAs to the patient's brain) by identifying the patients who may benefit from injection, and (3) reducing healthcare costs, patients' discomfort, and caregivers' workload.

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Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods

Cited by 13 publications

References 80 publications

Ensemble CNN and Uncertainty Modeling to Improve Automatic Identification/Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Imaging

Ensemble CNN and Uncertainty Modeling to Improve Automatic Identification/Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Imaging

New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images

Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions

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