Detecting cerebral microbleeds via deep learning with features enhancement by reusing ground truth

Li, Tianfu; Zou, Yan; Bai, Peng; Wang, Huawei; Chen, Xingliang; Meng, Zhanao; Kang, Zhuang; Zhou, Guofu

doi:10.1016/j.cmpb.2021.106051

Cited by 16 publications

(13 citation statements)

References 25 publications

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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%

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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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Section: Cmb Ratingmentioning

confidence: 99%

“…DICOM to JPG conversion is an excellent example of lossy data conversion technique, which might influence further processing stages. It was done by [19]. Although DICOM or NIFTY formats might be considered not developerfriendly, working on original image matrices should be a standard.…”

Section: Pre-processingmentioning

confidence: 99%

Review of methods for automatic cerebral microbleeds detection

Ferlin¹,

Klawikowska²,

Grzywińska³

et al. 2023

Preprint

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“… Rashid et al (2021) proposed DEEPMIR to detect CMBs and iron deposits, and an average SEN of between 84%–88% was achieved. Li et al (2021b) used feature enhancement in CMB detection, and an SEN of 90.00% was achieved, suggesting that feature enhancement can be a helpful algorithm to enhance the deep learning model. Dou et al (2016) performed CMB detection via 3D convolutional neural networks, and a sensitivity of 92.31% was achieved.…”

Section: Related Workmentioning

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%

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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“…In the past few years, the analysis of medical images based on ML has gained significant importance in scientific research. In particular, with the progress of computer vision, researchers are encouraged to develop various systems for the analysis, correlation, and interpretation of medical images [ 15 , 16 , 17 ] such as convolutional neural networks for brain image segmentation [ 18 , 19 , 20 , 21 ]; for brain tumor detection and classification [ 22 , 23 , 24 ]; medical image registration, fusion, and annotation [ 25 , 26 , 27 , 28 , 29 ]; computer-aided diagnosis (CAD) systems [ 30 , 31 , 32 , 33 , 34 , 35 ]; and the automatic detection of micro-bleeds in a medical image [ 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology

Simović¹,

Lutovac-Banduka

Lekić

et al. 2022

Diagnostics

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The smart visualization of medical images (SVMI) model is based on multi-detector computed tomography (MDCT) data sets and can provide a clearer view of changes in the brain, such as tumors (expansive changes), bleeding, and ischemia on native imaging (i.e., a non-contrast MDCT scan). The new SVMI method provides a more precise representation of the brain image by hiding pixels that are not carrying information and rescaling and coloring the range of pixels essential for detecting and visualizing the disease. In addition, SVMI can be used to avoid the additional exposure of patients to ionizing radiation, which can lead to the occurrence of allergic reactions due to the contrast media administration. Results of the SVMI model were compared with the final diagnosis of the disease after additional diagnostics and confirmation by neuroradiologists, who are highly trained physicians with many years of experience. The application of the realized and presented SVMI model can optimize the engagement of material, medical, and human resources and has the potential for general application in medical training, education, and clinical research.

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Detecting cerebral microbleeds via deep learning with features enhancement by reusing ground truth

Cited by 16 publications

References 25 publications

Review of methods for automatic cerebral microbleeds detection

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

Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology

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