scholarly journals Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis

2020 ◽  
pp. 135245852092136 ◽  
Author(s):  
Ivan Coronado ◽  
Refaat E Gabr ◽  
Ponnada A Narayana
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Network Performance ◽  
Three Dimensional ◽  
Proton Density ◽  
Dice Similarity Coefficient ◽  
Lesion Volume ◽  
Imaging Data ◽  
Post Contrast ◽  
Fluid Attenuated Inversion Recovery

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients. Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing–remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume. Results: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size. Conclusion: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.

Detection and clinical correlation of leukocortical lesions in pediatric-onset multiple sclerosis on multi-contrast MRI

2018 ◽  
Vol 25 (7) ◽  
pp. 980-986 ◽  
Author(s):  
Josefina Maranzano ◽  
Christine Till ◽  
Haz-Edine Assemlal ◽  
Vladimir Fonov ◽  
Robert Brown ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Disease Duration ◽  
Three Dimensional ◽  
High Rate ◽  
Proton Density ◽  
Lesion Volume ◽  
3T Mri ◽  
Fluid Attenuated Inversion Recovery ◽  
Magnetic Resonance Imaging Mri ◽  
Pediatric Onset

Objective: To determine the frequency of cortical lesions (CLs) in patients with pediatric-onset multiple sclerosis (POMS) using multi-contrast magnetic resonance imaging (MRI), and the relationship between frontal CL load and upper limb dexterity assessed with the Nine-Hole Peg Test (9-HPT). Methods: Participants completed the 9-HPT and were imaged on a 3T MRI scanner to collect T1-weighted three-dimensional (3D) magnetization prepared rapid gradient echo (MPRAGE), proton density–weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) images. CLs were manually segmented using all MRI contrasts. Results: We enrolled 24 participants with POMS (mean (standard deviation) age at first symptom: 13.3 (±2.7) years; mean age at scan: 18.8 (±3) years; mean disease duration of 5 (±3.2) years). A total of 391 CLs (mean, 16.3 ± 27.2; median, 7) were identified in 19 of 24 POMS patients (79%). The total number of CLs was positively associated with white matter lesion volume ( p = 0.04) but not with thalamic volume, age at the time of the scan, or disease duration. The number of frontal CLs was associated with slower performance on the 9-HPT ( p = 0.05). Conclusion: Multi-contrast 3T MRI led to a high rate of CL detection, demonstrating that cortical pathology occurs even in pediatric-onset disease. Frontal lobe CL count was associated with reduced manual dexterity, indicating that these CLs are clinically relevant.

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

T1 hypointense lesion load in secondary progressive multiple sclerosis: a comparison of pre versus post contrast loads and of manual versus semi automated threshold techniques for lesion segmentation

1998 ◽  
Vol 4 (5) ◽  
pp. 408-412 ◽  
Author(s):  
J I O'Riordan ◽  
M Gawne Cain ◽  
A Coles ◽  
L Wang ◽  
D AS Compston ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Strong Correlation ◽  
Measurement Techniques ◽  
Progressive Multiple Sclerosis ◽  
Secondary Progressive Multiple Sclerosis ◽  
Lesion Volume ◽  
Mri Findings ◽  
Lesion Load ◽  
Post Contrast ◽  
Secondary Progressive

Magnetic resonance imaging (MRI) is increasingly being used as a monitoring tool for disease activity in therapeutic trials in multiple sclerosis. There is, however, only a limited relationship between MRI findings and clinical outcome measurements. It has been suggested that hypointense lesion load on T1 weighted imaging has a better correlation with disability than the more conventional T2 hyper intense lesion load. This study was undertaken to (i) evaluate different measurement techniques used to quantify T1 hypointense lesion load, and (ii) to compare lesion load as measured using different parameters and disability. Twenty-five patients with secondary progressive multiple sclerosis, mean age of 40 years (23-57), mean EDSS 5.7 (4-7) were analysed. T2 lesion load on FSE correlated well with both the hypointense lesion load on T1 pre-gadolinium (r=0.8, P50.0001) and T1 post-gadolinium (r=0.8, P50.0001) but less so with the enhancing lesion load (r=0.4, P50.05). There was a very strong correlation with T1 hypo-intense lesion volume pre and post gadolinium (r=0.96, P50.001). However, the EDSS was not correlated with the T2 lesion load (r=70.27, P=0.2), T1 pre-gadolinium load (r=70.3, P=0.1), T1 post gadolinium load (r=70.4, P=0.7) and enhancing lesion load (r=70.28, P=0.2), or with the degree of hypointensity of T1 weighted images determined using the threshold technique. There is a strong correlation between T1 hypointense lesion volume both pre and post gadolinium and also between T1 and T2 lesion volumes.

scholarly journals Improvement of Multiparametric MR Image Segmentation by Augmenting the Data With Generative Adversarial Networks for Glioma Patients

2021 ◽  
Vol 14 ◽  
Author(s):  
Eric Nathan Carver ◽  
Zhenzhen Dai ◽  
Evan Liang ◽  
James Snyder ◽  
Ning Wen
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Model Performance ◽  
Image Data ◽  
Generative Adversarial Networks ◽  
Dice Similarity Coefficient ◽  
Generative Adversarial Network ◽  
Post Contrast ◽  
Adversarial Network ◽  
Fluid Attenuated Inversion Recovery

Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. MRI plays an essential role in the diagnosis and treatment assessment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigated the creation of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (Flair) MR images. These synthetic MR (synMR) images were assessed quantitatively with four metrics. The synMR images were also assessed qualitatively by an authoring physician with notions that synMR possessed realism in its portrayal of structural boundaries but struggled to accurately depict tumor heterogeneity. Additionally, this study investigated the synMR images created by generative adversarial network (GAN) to overcome the lack of annotated medical image data in training U-Nets to segment enhancing tumor, whole tumor, and tumor core regions on gliomas. Multiple two-dimensional (2D) U-Nets were trained with original BraTS data and differing subsets of the synMR images. Dice similarity coefficient (DSC) was used as the loss function during training as well a quantitative metric. Additionally, Hausdorff Distance 95% CI (HD) was used to judge the quality of the contours created by these U-Nets. The model performance was improved in both DSC and HD when incorporating synMR in the training set. In summary, this study showed the ability to generate high quality Flair, T2, T1, and T1CE synMR images using GAN. Using synMR images showed encouraging results to improve the U-Net segmentation performance and shows potential to address the scarcity of annotated medical images.

scholarly journals Deep learning classifiers for near infrared spectral imaging: a tutorial

2020 ◽  
Author(s):  
Jun-Li Xu ◽  
Cecilia Riccioli ◽  
Ana Herrero-Langreo ◽  
Aoife Gowen
Keyword(s):  
Deep Learning ◽  
Visual Recognition ◽  
Near Infrared ◽  
Three Dimensional ◽  
Spectral Imaging ◽  
Learning Approaches ◽  
Imaging Data ◽  
Spectral Image ◽  
Background Removal ◽  
Wide Range

Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.

scholarly journals Applying Deep Learning to Accelerated Clinical Brain Magnetic Resonance Imaging for Multiple Sclerosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Ashika Mani ◽  
Tales Santini ◽  
Radhika Puppala ◽  
Megan Dahl ◽  
Shruthi Venkatesh ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Repeated Measures ◽  
Financial Burden ◽  
Brain Magnetic Resonance Imaging ◽  
Lesion Volume ◽  
Patient Reported

Background: Magnetic resonance (MR) scans are routine clinical procedures for monitoring people with multiple sclerosis (PwMS). Patient discomfort, timely scheduling, and financial burden motivate the need to accelerate MR scan time. We examined the clinical application of a deep learning (DL) model in restoring the image quality of accelerated routine clinical brain MR scans for PwMS.Methods: We acquired fast 3D T1w BRAVO and fast 3D T2w FLAIR MRI sequences (half the phase encodes and half the number of slices) in parallel to conventional parameters. Using a subset of the scans, we trained a DL model to generate images from fast scans with quality similar to the conventional scans and then applied the model to the remaining scans. We calculated clinically relevant T1w volumetrics (normalized whole brain, thalamic, gray matter, and white matter volume) for all scans and T2 lesion volume in a sub-analysis. We performed paired t-tests comparing conventional, fast, and fast with DL for these volumetrics, and fit repeated measures mixed-effects models to test for differences in correlations between volumetrics and clinically relevant patient-reported outcomes (PRO).Results: We found statistically significant but small differences between conventional and fast scans with DL for all T1w volumetrics. There was no difference in the extent to which the key T1w volumetrics correlated with clinically relevant PROs of MS symptom burden and neurological disability.Conclusion: A deep learning model that improves the image quality of the accelerated routine clinical brain MR scans has the potential to inform clinically relevant outcomes in MS.

scholarly journals A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning

2021 ◽  
Author(s):  
Wing Keung Cheung ◽  
Robert Bell ◽  
Arjun Nair ◽  
Leon Menezies ◽  
Riyaz Patel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Arteries ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Regions Of Interest ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Two Dimensional ◽  
Computed Tomography Images ◽  
Graphical Processing

AbstractA fully automatic two-dimensional Unet model is proposed to segment aorta and coronary arteries in computed tomography images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Furthermore, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed within hospital computer networks where graphical processing units are typically not available.

Gray matter trophism, cognitive impairment, and depression in patients with multiple sclerosis

2017 ◽  
Vol 23 (14) ◽  
pp. 1864-1874 ◽  
Author(s):  
Emanuele Pravatà ◽  
Maria A Rocca ◽  
Paola Valsasina ◽  
Gianna C Riccitelli ◽  
Claudio Gobbi ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Impairment ◽  
Gray Matter ◽  
Rating Scale ◽  
Three Dimensional ◽  
Emotional Behavior ◽  
Cortical Atrophy ◽  
Lesion Volume ◽  
Cortical Thinning ◽  
C Statistic

Background: Cognitive impairment and depression frequently affects patients with multiple sclerosis (MS). However, the relationship between the occurrence of depression and cognitive impairment and the development of cortical atrophy has not been fully elucidated yet. Objectives: To investigate the association of cortical and deep gray matter (GM) volume with depression and cognitive impairment in MS. Methods: Three-dimensional (3D) T1-weighted scans were obtained from 126 MS patients and 59 matched healthy controls. Cognitive impairment was assessed using the Brief Repeatable Battery of Neuropsychological Tests and depression with the Montgomery-Asberg Depression Rating Scale (MADRS). Using FreeSurfer and FIRST software, we assessed cortical thickness (CTh) and deep GM volumetry. Magnetic resonance imaging (MRI) variables explaining depression and cognitive impairment were investigated using factorial and classification analysis. Multivariate regression models correlated GM abnormalities with symptoms severity. Results: Compared with controls, MS patients exhibited widespread bilateral cortical thinning involving all brain lobes. Depressed MS showed selective CTh decrease in fronto-temporal regions, whereas cognitive impairment MS exhibited widespread fronto-parietal cortical and subcortical GM atrophy. Frontal cortical thinning was the best predictor of depression ( C-statistic = 0.7), whereas thinning of the right precuneus and high T2 lesion volume best predicted cognitive impairment ( C-statistic = 0.8). MADRS severity correlated with right entorhinal cortex thinning, whereas cognitive impairment severity correlated with left entorhinal and thalamus atrophy. Conclusion: MS-related depression is linked to circumscribed CTh changes in areas deputed to emotional behavior, whereas cognitive impairment is correlated with cortical and subcortical GM atrophy of circuits involved in cognition.

scholarly journals Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI

2020 ◽  
Vol 14 ◽  
Author(s):  
Chenyi Zeng ◽  
Lin Gu ◽  
Zhenzhong Liu ◽  
Shen Zhao
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Dice Similarity Coefficient ◽  
Learning Approaches ◽  
Review Of The Literature ◽  
Lesion Segmentation ◽  
Segmentation Methods ◽  
Future Direction

In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.

Sign in / Sign up

Export Citation Format

Share Document