Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review

• URL: http://arxiv.org/abs/2204.01411v1
• Date: Mon, 4 Apr 2022 12:01:39 GMT
• Title: Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review
• Authors: Jiyang Jiang, Dadong Wang, Yang Song, Perminder S. Sachdev, Wei Wen
• Abstract summary: Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS) and lacunes of presumed vascular origin.
• Score: 17.021934273900182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy-one classical image processing, classical machine learning, and deep learning studies were identified. CMB and PVS have been better studied, compared to lacunes. While good performance metrics have been achieved in local test datasets, there have not been generalisable pipelines validated in different research or clinical cohorts. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in training data. Future studies could consider pooling data from multiple sources to increase diversity, and validating the performance of the methods using both image processing metrics and associations with clinical measures.

Related papers

• Enhancing Retinal Disease Classification from OCTA Images via Active Learning Techniques [0.8035416719640156]
  Eye diseases are common in older Americans and can lead to decreased vision and blindness. Recent advancements in imaging technologies allow clinicians to capture high-quality images of the retinal blood vessels via Optical Coherence Tomography Angiography ( OCTA) OCTA provides detailed vascular imaging as compared to the solely structural information obtained by common OCT imaging.
  arXiv  Detail & Related papers  (2024-07-21T23:24:49Z)
• Predicting risk of cardiovascular disease using retinal OCT imaging [40.71667870702634]
  We investigated the potential of optical coherence tomography as an additional imaging technique to predict future cardiovascular disease (CVD) We utilised a self-supervised deep learning approach based on Variational Autoencoders (VAE) to learn low-dimensional representations of high-dimensional 3D OCT images. The choroidal layer visible in OCT images was identified as an important predictor of future CVD events using a novel approach to model explanability.
  arXiv  Detail & Related papers  (2024-03-26T14:42:46Z)
• Identification of Hemorrhage and Infarct Lesions on Brain CT Images using Deep Learning [3.5263317784479953]
  Current standard for manual annotations of abnormal brain tissue on head NCCT scans involves significant disadvantages. determining Intracranial hemorrhage (ICH) and infarct can be challenging due to image texture, volume size, and scan quality variability. This retrospective validation study evaluated a DL-based algorithm identifying ICH and infarct from head-NCCT scans.
  arXiv  Detail & Related papers  (2023-07-10T09:00:12Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence [20.415917092103033]
  Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD) The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing.
  arXiv  Detail & Related papers  (2022-10-26T10:34:20Z)
• FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain [0.8376091455761259]
  A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance.
  arXiv  Detail & Related papers  (2022-08-30T16:06:07Z)
• SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images [62.60956024215873]
  Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. This study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin diseases classification.
  arXiv  Detail & Related papers  (2022-03-22T06:54:29Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.