Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance

Myung, Min Jae; Lee, Kyung Mi; Kim, Hyug Gi; Oh, Jeong-Ik; Lee, Ji Young; Shin, In Sik; Kim, Eui Jong; Lee, Jin San

doi:10.1016/j.jstrokecerebrovasdis.2021.105886

Cited by 21 publications

(18 citation statements)

References 21 publications

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“…You Only Look Once (YOLO) V2 28 , a state-of-the-art CNN object detection algorithm that can simultaneously detect the locations of objects in input images and classify them into different categories, was used for the DLA architecture. A YOLO V2 network for each five-fold cross-validation was initialized using the transfer learning method based on pre-trained ResNet-50 29 , similar to that in a previous study 30 , with the following parameters: seven anchor boxes, Adam optimizer, mini-batch size of 64, initial learning rate of 1 × 10 −3 , factor for L2 regularization of 1 × 10 −4 , and 1,000 epochs at maximum. To compensate for the lack of training data, random image rotations (0°, 90°, 180°, and 270°) and left–right flip processing were implemented.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based detection algorithm for brain metastases on black blood imaging

Lee

Kim

et al. 2022

Sci Rep

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Brain metastases (BM) are the most common intracranial tumors, and their prevalence is increasing. High-resolution black-blood (BB) imaging was used to complement the conventional contrast-enhanced 3D gradient-echo imaging to detect BM. In this study, we propose an efficient deep learning algorithm (DLA) for BM detection in BB imaging with contrast enhancement scans, and assess the efficacy of an automatic detection algorithm for BM. A total of 113 BM participants with 585 metastases were included in the training cohort for five-fold cross-validation. The You Only Look Once (YOLO) V2 network was trained with 3D BB sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) images to investigate the BM detection. For the observer performance, two board-certified radiologists and two second-year radiology residents detected the BM and recorded the reading time. For the training cohort, the overall performance of the five-fold cross-validation was 87.95%, 24.82%, 19.35%, 14.48, and 18.40 for sensitivity, precision, F1-Score, the false positive average for the BM dataset, and the false positive average for the normal individual dataset, respectively. For the comparison of reading time with and without DLA, the average reading time was reduced by 20.86% in the range of 15.22–25.77%. The proposed method has the potential to detect BM with a high sensitivity and has a limited number of false positives using BB imaging.

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Section: Methodsmentioning

confidence: 99%

Deep learning-based detection algorithm for brain metastases on black blood imaging

Lee

Kim

et al. 2022

Sci Rep

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“…Although their reliability based on intra-and inter-observer agreement is reported, details of the methods used are usually not described [109]. [32,33], Traumatic Brain Injury (TBI) [45,29,5,44,140], stroke [31,5,73,20], Intracerebral Haemorrhages (ICH) [34,20], gliomas [26,51,17], hemodialysis cases [5], Cerebral Amyloid Angiopathy (CAA) [34], atherosclerosis [6], or did not distinguish any particular disease besides the appearance of CMBs [1,30,42,43,3,80,79,24,18,19,76,22,13,72,20,126,138,137]. Datasets used in the first category of researches focused on AD [81,82,83,36,84,85], SMART [37], TBI [86], stroke [86,…”

Section: Cmb Ratingmentioning

confidence: 99%

Review of methods for automatic cerebral microbleeds detection

Ferlin¹,

Klawikowska²,

Grzywińska³

et al. 2023

Preprint

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Cerebral microbleeds detection is an important and challenging task. With the gaining popularity of the MRI, the ability to detect cerebral microbleeds also raises. Unfortunately, for radiologists, it is a time-consuming and laborious procedure. For this reason, various solutions to automate this process have been proposed for several years, but none of them is currently used in medical practice. In this context, the need to systematize the existing knowledge and best practices has been recognized as a factor facilitating the imminent synthesis of a real CMBs detection system practically applicable in medicine. To the best of our knowledge, all available publications regarding automatic cerebral microbleeds detection have been gathered, described, and assessed in this paper in order to distinguish the current research state and provide a starting point for future studies.

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“…There is a significant need to implement automated segmentation of CMBs in SWI sequences, which is difficult but possible. Myung et al (2021) proposed a two-stage approach to conduct CMB detection based on the you only look once (YOLO) model, which achieved an SEN of 80.96%. Rashid et al (2021) proposed DEEPMIR to detect CMBs and iron deposits, and an average SEN of between 84%–88% was achieved.…”

Section: Related Workmentioning

confidence: 99%

“…However, the automated segmentation of microlesions represented by CMBs is a more difficult and challenging clinical task because CMBs are widely distributed throughout the brain. They are not only extremely small but also share a high degree of visual similarity with CMB analogues (such as calcification, rust, and veins) ( Myung et al, 2021 ). In addition, CMBs present a blooming effect on MRI images, meaning that the volume of CMBs increase with increasing echo time, and various acquisition settings may affect CMB sizes ( Rashid et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is a significant need to implement automated segmentation of CMBs in SWI sequences, which is difficult but possible. However, most of the previous studies are limited to CMB target detection ( Dou et al, 2016 ; Liu et al, 2019 ; Al-masni et al, 2020 ; Li et al, 2021b ; Myung et al, 2021 ; Rashid et al, 2021 ), and there is a lack of related research on the automatic segmentation of CMBs.…”

Section: Introductionmentioning

confidence: 99%

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Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging

Wei

Chen

Huang

et al. 2022

Front. Bioeng. Biotechnol.

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Aim: The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system.Materials and Methods: From a research perspective, we focused on the automated segmentation of CMB targets in susceptibility weighted imaging (SWI) for the first time and then constructed a deep learning network focused on the segmentation of micro-objects. We collected and marked clinical datasets and proposed a new medical micro-object cascade network (MMOC-Net). In the first stage, U-Net was utilized to select the region of interest (ROI). In the second stage, we utilized a full-resolution network (FRN) to complete fine segmentation. We also incorporated residual atrous spatial pyramid pooling (R-ASPP) and a new joint loss function.Results: The most suitable segmentation result was achieved with a ROI size of 32 × 32. To verify the validity of each part of the method, ablation studies were performed, which showed that the best segmentation results were obtained when FRN, R-ASPP and the combined loss function were used simultaneously. Under these conditions, the obtained Dice similarity coefficient (DSC) value was 87.93% and the F2-score (F2) value was 90.69%. We also innovatively developed a visual clinical diagnosis system that can provide effective support for clinical diagnosis and treatment decisions.Conclusions: We created the MMOC-Net method to perform the automated segmentation task of CMBs in an SWI and obtained better segmentation performance; hence, this pioneering method has research significance.

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Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance

Cited by 21 publications

References 21 publications

Deep learning-based detection algorithm for brain metastases on black blood imaging

Deep learning-based detection algorithm for brain metastases on black blood imaging

Review of methods for automatic cerebral microbleeds detection

Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging

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