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Ejiyi CJ, Qin Z, Ejiyi MB, Ukwuoma C, Ejiyi TU, Muoka GW, Gyarteng ESA, Bamisile OO. MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation. Comput Biol Med 2024; 179:108847. [PMID: 39004046 DOI: 10.1016/j.compbiomed.2024.108847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/28/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) - an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection. As the depth increases, our proposed spatial multi-head attention (SMA) is added to facilitate inter-layer communication and information exchange, enabling the network to effectively capture long-range dependencies and global context. MACCoM is also equipped with a convolutional cross-mixer we proposed to enhance the feature extraction capability of the model. By incorporating these modules, we effectively combine semantically similar features and reduce artifacts during the early stages of training. Experimental results on 4 biomedical datasets crafted from 3 datasets of varying modalities consistently demonstrate that MACCoM outperforms or matches state-of-the-art baselines in the segmentation tasks. With Breast Ultrasound Image (BUSI), MACCoM recorded 99.06 % Jaccard, 77.58 % Dice, and 93.92 % Accuracy, while recording 99.50 %, 98.44 %, and 99.29 % respectively for Jaccard, Dice, and Accuracy on the Chest X-ray (CXR) images used. The Jaccard, Dice, and Accuracy for the High-Resolution Fundus (HRF) images are 95.77 %, 74.35 %, and 95.95 % respectively. The findings here highlight MACCoM's effectiveness in improving segmentation performance and its valuable potential in image analysis.
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Affiliation(s)
- Chukwuebuka Joseph Ejiyi
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Chengdu, China
| | - Zhen Qin
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
| | | | - Chiagoziem Ukwuoma
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Thomas Ugochukwu Ejiyi
- Department of Pure and Industrial Chemistry, University of Nigeria Nsukka, Nsukka, Enugu State, Nigeria
| | - Gladys Wavinya Muoka
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Emmanuel S A Gyarteng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Olusola O Bamisile
- Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Chengdu, China
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Sobootian DJ, Bronzlik P, Spineli LM, Becker LS, Winther HB, Bueltmann E. Convolutional Neural Network for Fully Automated Cerebellar Volumetry in Children in Comparison to Manual Segmentation and Developmental Trajectory of Cerebellar Volumes. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1074-1085. [PMID: 37833550 PMCID: PMC11102395 DOI: 10.1007/s12311-023-01609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
The purpose of this study was to develop a fully automated and reliable volumetry of the cerebellum of children during infancy and childhood using deep learning algorithms in comparison to manual segmentation. In addition, the clinical usefulness of measuring the cerebellar volume is shown. One hundred patients (0 to 16.3 years old) without infratentorial signal abnormalities on conventional MRI were retrospectively selected from our pool of pediatric MRI examinations. Based on a routinely acquired 3D T1-weighted magnetization prepared rapid gradient echo (MPRAGE) sequence, the cerebella were manually segmented using ITK-SNAP. The data set of all 100 cases was divided into four splits (four-fold cross-validation) to train the network (NN) to delineate the boundaries of the cerebellum. First, the accuracy of the newly created neural network was compared with the manual segmentation. Secondly, age-related volume changes were investigated. Our trained NN achieved an excellent Spearman correlation coefficient of 0.99, a Dice Coefficient of 95.0 ± 2.1%, and an intersection over union (IoU) of 90.6 ± 3.8%. Cerebellar volume increased continuously with age, showing an exponentially rapid growth within the first year of life. Using a convolutional neural network, it was possible to achieve reliable, fully automated cerebellar volume measurements in childhood and infancy, even when based on a relatively small cohort. In this preliminary study, age-dependent cerebellar volume changes could be acquired.
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Affiliation(s)
- Daria Juliane Sobootian
- Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Paul Bronzlik
- Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Loukia M Spineli
- Midwifery Research and Education Unit, Hannover Medical School, Hannover, Germany
| | - Lena Sophie Becker
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Hinrich Boy Winther
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Eva Bueltmann
- Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany.
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Yamada S, Ito H, Matsumasa H, Ii S, Otani T, Tanikawa M, Iseki C, Watanabe Y, Wada S, Oshima M, Mase M. Automatic assessment of disproportionately enlarged subarachnoid-space hydrocephalus from 3D MRI using two deep learning models. Front Aging Neurosci 2024; 16:1362637. [PMID: 38560023 PMCID: PMC10978765 DOI: 10.3389/fnagi.2024.1362637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 02/22/2024] [Indexed: 04/04/2024] Open
Abstract
Background Disproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a key feature for Hakim disease (idiopathic normal pressure hydrocephalus: iNPH), but subjectively evaluated. To develop automatic quantitative assessment of DESH with automatic segmentation using combined deep learning models. Methods This study included 180 participants (42 Hakim patients, 138 healthy volunteers; 78 males, 102 females). Overall, 159 three-dimensional (3D) T1-weighted and 180 T2-weighted MRIs were included. As a semantic segmentation, 3D MRIs were automatically segmented in the total ventricles, total subarachnoid space (SAS), high-convexity SAS, and Sylvian fissure and basal cistern on the 3D U-Net model. As an image classification, DESH, ventricular dilatation (VD), tightened sulci in the high convexities (THC), and Sylvian fissure dilatation (SFD) were automatically assessed on the multimodal convolutional neural network (CNN) model. For both deep learning models, 110 T1- and 130 T2-weighted MRIs were used for training, 30 T1- and 30 T2-weighted MRIs for internal validation, and the remaining 19 T1- and 20 T2-weighted MRIs for external validation. Dice score was calculated as (overlapping area) × 2/total area. Results Automatic region extraction from 3D T1- and T2-weighted MRI was accurate for the total ventricles (mean Dice scores: 0.85 and 0.83), Sylvian fissure and basal cistern (0.70 and 0.69), and high-convexity SAS (0.68 and 0.60), respectively. Automatic determination of DESH, VD, THC, and SFD from the segmented regions on the multimodal CNN model was sufficiently reliable; all of the mean softmax probability scores were exceeded by 0.95. All of the areas under the receiver-operating characteristic curves of the DESH, Venthi, and Sylhi indexes calculated by the segmented regions for detecting DESH were exceeded by 0.97. Conclusion Using 3D U-Net and a multimodal CNN, DESH was automatically detected with automatically segmented regions from 3D MRIs. Our developed diagnostic support tool can improve the precision of Hakim disease (iNPH) diagnosis.
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Affiliation(s)
- Shigeki Yamada
- Department of Neurosurgery, Nagoya City University Graduate School of Medical Science, Nagoya, Japan
- Interfaculty Initiative in Information Studies/Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Hirotaka Ito
- Medical System Research & Development Center, FUJIFILM Corporation, Tokyo, Japan
| | - Hironori Matsumasa
- Medical System Research & Development Center, FUJIFILM Corporation, Tokyo, Japan
| | - Satoshi Ii
- Faculty of System Design, Tokyo Metropolitan University, Tokyo, Japan
| | - Tomohiro Otani
- Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Motoki Tanikawa
- Department of Neurosurgery, Nagoya City University Graduate School of Medical Science, Nagoya, Japan
| | - Chifumi Iseki
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai, Japan
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata, Japan
| | - Yoshiyuki Watanabe
- Department of Radiology, Shiga University of Medical Science, Otsu, Japan
| | - Shigeo Wada
- Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Marie Oshima
- Interfaculty Initiative in Information Studies/Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Mitsuhito Mase
- Department of Neurosurgery, Nagoya City University Graduate School of Medical Science, Nagoya, Japan
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Goo HW, Park SH. Fast Quantitative Magnetic Resonance Imaging Evaluation of Hydrocephalus Using 3-Dimensional Fluid-Attenuated Inversion Recovery: Initial Experience. J Comput Assist Tomogr 2024; 48:292-297. [PMID: 37621082 DOI: 10.1097/rct.0000000000001539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
OBJECTIVE This study aimed to demonstrate the initial experience of using fast quantitative magnetic resonance imaging (MRI) to evaluate hydrocephalus. METHODS A total of 109 brain MRI volumetry examinations (acquisition time, 7 minutes 30 seconds) were performed in 72 patients with hydrocephalus. From the measured ventricular system and brain volumes, ventricle-brain volume percentage was calculated to standardize hydrocephalus severity (processing time, <5 minutes). The obtained values were categorized into no, mild, and severe based on the fronto-occipital horn ratio (FOHR) and the ventricle-brain volume percentages reported in the literature. The measured volumes and percentages were compared between patients with mild hydrocephalus and those with severe hydrocephalus. The diagnostic performance of brain hydrocephalus MRI volumetry was evaluated using receiver operating characteristic curve analysis. RESULTS Ventricular volumes and ventricle-brain volume percentages were significantly higher in in patients with severe hydrocephalus than in those with mild hydrocephalus (FOHR-based severity: 352.6 ± 165.6 cm 3 vs 149.1 ± 78.5 cm 3 , P < 0.001, and 26.8% [20.8%-33.1%] vs 12.1% ± 6.0%, P < 0.001; percentage-based severity: 359.5 ± 143.3 cm 3 vs 137.0 ± 62.9 cm 3 , P < 0.001, and 26.8% [21.8%-33.1%] vs 11.3% ± 4.2%, P < 0.001, respectively), whereas brain volumes were significantly lower in patients with severe hydrocephalus than in those with mild hydrocephalus (FOHR-based severity: 878.1 ± 363.5 cm 3 vs 1130.1 cm 3 [912.1-1244.2 cm 3 ], P = 0.006; percentage-based severity: 896.2 ± 324.6 cm 3 vs 1142.3 cm 3 [944.2-1246.6 cm 3 ], P = 0.005, respectively). The ventricle-brain volume percentage was a good diagnostic parameter for evaluating the degree of hydrocephalus (area under the curve, 0.855; 95% confidence interval, 0.719-0.990; P < 0.001). CONCLUSIONS Brain MRI volumetry can be used to evaluate hydrocephalus severity and may provide guide interpretation because of its rapid acquisition and postprocessing times.
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Affiliation(s)
- Hyun Woo Goo
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Sabeti M, Alikhani S, Shakoor M, Boostani R, Moradi E. Automatic determination of ventricular indices in hydrocephalic pediatric brain CT scan. INTERDISCIPLINARY NEUROSURGERY 2023. [DOI: 10.1016/j.inat.2022.101675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Atasever S, Azginoglu N, Terzi DS, Terzi R. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clin Imaging 2023; 94:18-41. [PMID: 36462229 DOI: 10.1016/j.clinimag.2022.11.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/17/2022] [Accepted: 11/01/2022] [Indexed: 11/13/2022]
Abstract
This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments in transfer learning (TL). Unlike previous studies, this survey grouped the last five years of current studies for the period between January 2017 and February 2021 according to different anatomical regions and detailed the modality, medical task, TL method, source data, target data, and public or private datasets used in medical imaging. Also, it provides readers with detailed information on technical challenges, opportunities, and future research trends. In this way, an overview of recent developments is provided to help researchers to select the most effective and efficient methods and access widely used and publicly available medical datasets, research gaps, and limitations of the available literature.
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Affiliation(s)
- Sema Atasever
- Computer Engineering Department, Nevsehir Hacı Bektas Veli University, Nevsehir, Turkey.
| | - Nuh Azginoglu
- Computer Engineering Department, Kayseri University, Kayseri, Turkey.
| | | | - Ramazan Terzi
- Computer Engineering Department, Amasya University, Amasya, Turkey.
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Huang Y, Moreno R, Malani R, Meng A, Swinburne N, Holodny AI, Choi Y, Rusinek H, Golomb JB, George A, Parra LC, Young RJ. Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment. J Digit Imaging 2022; 35:1662-1672. [PMID: 35581409 PMCID: PMC9712867 DOI: 10.1007/s10278-022-00654-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/11/2022] [Accepted: 05/02/2022] [Indexed: 11/25/2022] Open
Abstract
In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectively at a single clinical site from patients referred for any reason. This diagnostic dataset was enriched to have 259 hydrocephalus cases. A 3D convolutional neural network was trained on 16 manually segmented exams (ten hydrocephalus) and subsequently used to automatically segment the remaining 480 exams and extract volumetric anatomical features. A linear classifier of these features was trained on 240 exams to detect cases of hydrocephalus that required treatment with surgical intervention. Performance was compared to four neuroradiologists on the remaining 240 exams. Performance was also evaluated on a separate screening dataset of 451 exams collected from a routine clinical population to predict the consensus reading from four neuroradiologists using images alone. The pipeline was also tested on an external dataset of 31 exams from a 2nd clinical site. The most discriminant features were the Magnetic Resonance Hydrocephalic Index (MRHI), ventricle volume, and the ratio between ventricle and brain volume. At matching sensitivity, the specificity of the machine and the neuroradiologists did not show significant differences for detection of hydrocephalus on either dataset (proportions test, p > 0.05). ROC performance compared favorably with the state-of-the-art (AUC 0.90-0.96), and replicated in the external validation. Hydrocephalus cases requiring treatment can be detected automatically from MRI in a heterogeneous patient population based on quantitative characterization of brain anatomy with performance comparable to that of neuroradiologists.
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Affiliation(s)
- Yu Huang
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Raquel Moreno
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Rachna Malani
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Alicia Meng
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Nathaniel Swinburne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Andrei I. Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Ye Choi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Henry Rusinek
- Department of Radiology, Grossman School of Medicine, New York University, New York, NY 10016 USA
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY 10016 USA
| | - James B. Golomb
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY 10016 USA
- Department of Neurology, Grossman School of Medicine, New York University, New York, NY 10016 USA
| | - Ajax George
- Department of Radiology, Grossman School of Medicine, New York University, New York, NY 10016 USA
| | - Lucas C. Parra
- Department of Biomedical Engineering, City College of New York, 160 Convent Ave, Steinman Hall Room 401, New York, NY 10031 USA
| | - Robert J. Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
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Arabahmadi M, Farahbakhsh R, Rezazadeh J. Deep Learning for Smart Healthcare-A Survey on Brain Tumor Detection from Medical Imaging. SENSORS (BASEL, SWITZERLAND) 2022; 22:1960. [PMID: 35271115 PMCID: PMC8915095 DOI: 10.3390/s22051960] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/18/2022] [Accepted: 02/28/2022] [Indexed: 12/13/2022]
Abstract
Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on "braintumor" website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.
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Affiliation(s)
| | - Reza Farahbakhsh
- Institut Polytechnique de Paris, Telecom SudParis, 91000 Evry, France;
| | - Javad Rezazadeh
- North Tehran Branch, Azad University, Tehran 1667914161, Iran;
- Kent Institute Australia, Sydney, NSW 2000, Australia
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Pringle C, Kilday JP, Kamaly-Asl I, Stivaros SM. The role of artificial intelligence in paediatric neuroradiology. Pediatr Radiol 2022; 52:2159-2172. [PMID: 35347371 PMCID: PMC9537195 DOI: 10.1007/s00247-022-05322-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/22/2021] [Accepted: 02/11/2022] [Indexed: 01/17/2023]
Abstract
Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.
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Affiliation(s)
- Catherine Pringle
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK
| | - John-Paul Kilday
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,The Centre for Paediatric, Teenage and Young Adult Cancer, Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Ian Kamaly-Asl
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,The Centre for Paediatric, Teenage and Young Adult Cancer, Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Stavros Michael Stivaros
- Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK. .,Department of Paediatric Radiology, Royal Manchester Children's Hospital, Central Manchester University Hospitals NHS Foundation Trust, Oxford Road, Manchester, M13 9WL, UK. .,The Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
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Rudhra B, Malu G, Sherly E, Mathew R. A Novel deep learning approach for the automated diagnosis of normal pressure hydrocephalus. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Normal Pressure Hydrocephalus (NPH), an Atypical Parkinsonian syndrome, is a neurological syndrome that mainly affects elderly people. This syndrome shows the symptoms of Parkinson’s disease (PD), such as walking impairment, dementia, impaired bladder control, and mental impairment. The Magnetic Resonance Imaging (MRI) is the aptest modality for the detection of the abnormal build-up of cerebrospinal fluid in the brain’s cavities or ventricles, which is the major cause of NPH. This work aims to develop an automated biomarker for NPH segmentation and classification (NPH-SC) that efficiently detect hydrocephalus using a deep learning-based approach. Removal of non-cerebral tissues (skull, scalp, and dura) and noise from brain images by skull stripping, unsharp-mask based edge sharpening, segmentation by marker-based watershed algorithm, and labelling are performed to improve the accuracy of the CNN based classification system. The brain ventricles are extracted using the external and internal markers and then fed into the convolutional neural networks (CNN) for classification. This automated NPH-SC model achieved a sensitivity of 96%, a specificity of 100%, and a validation accuracy of 97%. The prediction system, with the help of a CNN classifier, is used for the calculation of test accuracy of the system and obtained promising 98% accuracy.
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Affiliation(s)
- B Rudhra
- Indian Institute of Information Technology and Management, Trivandrum, India
| | - G Malu
- Indian Institute of Information Technology and Management, Trivandrum, India
| | - Elizabeth Sherly
- Indian Institute of Information Technology and Management, Trivandrum, India
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Valverde JM, Imani V, Abdollahzadeh A, De Feo R, Prakash M, Ciszek R, Tohka J. Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review. J Imaging 2021; 7:66. [PMID: 34460516 PMCID: PMC8321322 DOI: 10.3390/jimaging7040066] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/26/2021] [Accepted: 03/29/2021] [Indexed: 11/25/2022] Open
Abstract
(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.
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Affiliation(s)
| | | | | | | | | | | | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70150 Kuopio, Finland; (J.M.V.); (V.I.); (A.A.); (R.D.F.); (M.P.); (R.C.)
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