A deep learning algorithm for white matter hyperintensity lesion detection and segmentation

Deep Learning techniques to detect and analysis of multiple sclerosis through MRI: A systematic literature review

Deep learning (DL) techniques represent a rapidly advancing field within artificial intelligence, gaining significant prominence in the detection and analysis of various medical conditions through the analysis of medical data. This study presents a systematic literature review (SLR) focused on deep learning methods for the detection and analysis of multiple sclerosis (MS) using magnetic resonance imaging (MRI). The initial search identified 401 articles, which were rigorously screened, a selection of 82 highly relevant studies. These selected studies primarily concentrate on key areas such as multiple sclerosis, deep learning, convolutional neural networks (CNN), lesion segmentation, and classification, reflecting their alignment with the current state of the art. This review comprehensively examines diverse deep-learning approaches for MS detection and analysis, offering a valuable resource for researchers. Additionally, it presents key insights by summarizing these DL techniques for MS detection and analysis using MRI in a structured tabular format.

Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation

Introduction

White matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementia and stroke, even after accounting for vascular risk factors. Manual segmentation, while considered the ground truth, is both labor-intensive and time-consuming, limiting the generation of annotated WMH datasets. Un-annotated data are relatively available; however, the requirement of annotated data poses a challenge for developing supervised machine learning models.

Methods

To address this challenge, we implemented a multi-stage semi-supervised learning (M3SL) approach that first uses un-annotated data segmented by traditional processing methods ("bronze" and "silver" quality data) and then uses a smaller number of "gold"-standard annotations for model refinement. The M3SL approach enabled fine-tuning of the model weights with the gold-standard annotations. This approach was integrated into the training of a U-Net model for WMH segmentation. We used data from three scanner vendors (over more than five scanners) and from both cognitively normal (CN) adult and patients cohorts [with mild cognitive impairment and Alzheimer's disease (AD)].

Results

An analysis of WMH segmentation performance across both scanner and clinical stage (CN, MCI, AD) factors was conducted. We compared our results to both conventional and transfer-learning deep learning methods and observed better generalization with M3SL across different datasets. We evaluated several metrics (F-measure, IoU, and Hausdorff distance) and found significant improvements with our method compared to conventional (p < 0.001) and transfer-learning (p < 0.001).

Discussion

These findings suggest that automated, non-machine learning, tools have a role in a multi-stage learning framework and can reduce the impact of limited annotated data and, thus, enhance model performance.

Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high-dimensional features in imaging can assist doctors in clinical decision-making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct.

Clinical applications of deep learning in neuroinflammatory diseases: A scoping review

BACKGROUND

Deep learning (DL) is an artificial intelligence technology that has aroused much excitement for predictive medicine due to its ability to process raw data modalities such as images, text, and time series of signals.

OBJECTIVES

Here, we intend to give the clinical reader elements to understand this technology, taking neuroinflammatory diseases as an illustrative use case of clinical translation efforts. We reviewed the scope of this rapidly evolving field to get quantitative insights about which clinical applications concentrate the efforts and which data modalities are most commonly used.

METHODS

We queried the PubMed database for articles reporting DL algorithms for clinical applications in neuroinflammatory diseases and the radiology.healthairegister.com website for commercial algorithms.

RESULTS

The review included 148 articles published between 2018 and 2024 and five commercial algorithms. The clinical applications could be grouped as computer-aided diagnosis, individual prognosis, functional assessment, the segmentation of radiological structures, and the optimization of data acquisition. Our review highlighted important discrepancies in efforts. The segmentation of radiological structures and computer-aided diagnosis currently concentrate most efforts with an overrepresentation of imaging. Various model architectures have addressed different applications, relatively low volume of data, and diverse data modalities. We report the high-level technical characteristics of the algorithms and synthesize narratively the clinical applications. Predictive performances and some common a priori on this topic are finally discussed.

CONCLUSION

The currently reported efforts position DL as an information processing technology, enhancing existing modalities of paraclinical investigations and bringing perspectives to make innovative ones actionable for healthcare.

Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers

We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.

How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review

INTRODUCTION

Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI).

AIMS

Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow.

METHODS

Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI's six-steps, which include a tool's technical assessment, clinical validation, and integration.

RESULTS

We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth.

CONCLUSIONS

To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients' management of such tools remain almost unexplored.

Characterization of MRI White Matter Signal Abnormalities in the Pediatric Population

(1) Background and Purpose: The aim of this study was to retrospectively characterize WMSAs in an unselected patient cohort at a large pediatric neuroimaging facility, in order to learn more about the spectrum of the underlying disorders encountered in everyday clinical practice. (2) Materials and Methods: Radiology reports of 5166 consecutive patients with standard brain MRI (2006-2018) were searched for predefined keywords describing WMSAs. A neuroradiology specialist enrolled patients with WMSAs following a structured approach. Imaging characteristics, etiology (autoimmune disorders, non-genetic hypoxic and ischemic insults, traumatic white matter injuries, no final diagnosis due to insufficient clinical information, "non-specific" WMSAs, infectious white matter damage, leukodystrophies, toxic white matter injuries, inborn errors of metabolism, and white matter damage caused by tumor infiltration/cancer-like disease), and age/gender distribution were evaluated. (3) Results: Overall, WMSAs were found in 3.4% of pediatric patients scanned at our and referring hospitals within the ten-year study period. The majority were found in the supratentorial region only (87%) and were non-enhancing (78% of CE-MRI). WMSAs caused by autoimmune disorders formed the largest group (23%), followed by "non-specific" WMSAs (18%), as well as non-genetic hypoxic and ischemic insults (17%). The majority were therefore acquired as opposed to inherited. Etiology-based classification of WMSAs was affected by age but not by gender. In 17% of the study population, a definite diagnosis could not be established due to insufficient clinical information (mostly external radiology consults). (4) Conclusions: An "integrated diagnosis" that combines baseline demographics, including patient age as an important factor, clinical characteristics, and additional diagnostic workup with imaging patterns can be made in the majority of cases.

Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI

INTRODUCTION

Deep learning-based MRI reconstruction has recently been introduced to improve image quality. This study aimed to evaluate the performance of deep learning reconstruction in pediatric brain MRI.

METHODS

A total of 107 consecutive children who underwent 3.0 T brain MRI were included in this study. T2-weighted brain MRI was reconstructed using the three different reconstruction modes: deep learning reconstruction, conventional reconstruction with an intensity filter, and original T2 image without a filter. Two pediatric radiologists independently evaluated the following image quality parameters of three reconstructed images on a 5-point scale: overall image quality, image noisiness, sharpness of gray-white matter differentiation, truncation artifact, motion artifact, cerebrospinal fluid and vascular pulsation artifacts, and lesion conspicuity. The subjective image quality parameters were compared among the three reconstruction modes. Quantitative analysis of the signal uniformity using the coefficient of variation was performed for each reconstruction.

RESULTS

The overall image quality, noisiness, and gray-white matter sharpness were significantly better with deep learning reconstruction than with conventional or original reconstruction (all P < 0.001). Deep learning reconstruction had significantly fewer truncation artifacts than the other two reconstructions (all P < 0.001). Motion and pulsation artifacts showed no significant differences among the three reconstruction modes. For 36 lesions in 107 patients, lesion conspicuity was better with deep learning reconstruction than original reconstruction. Deep learning reconstruction showed lower signal variation compared to conventional and original reconstructions.

CONCLUSION

Deep learning reconstruction can reduce noise and truncation artifacts and improve lesion conspicuity and overall image quality in pediatric T2-weighted brain MRI.