Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks

Background: Osteoarthritis (OA) is a common degenerative joint inflammation which may lead to disability. Although OA is not lethal, this disease will remarkably affect patient’s mobility and their daily lives. Detecting OA at an early stage allows for early intervention and may slow down disease progression. Introduction: Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder–decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation. Methods: This study trained and compared 10 encoder–decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on the physical specifications and segmentation performances. Results: LadderNet has the least trainable parameters with model size of 5 MB. UNetVanilla crowned the best performances by having 0.8369, 0.9108, and 0.9097 on JSC, DSC, and MCC. Conclusion: UNetVanilla can be served as a benchmark for cartilage delineation in knee MR images while LadderNet served as alternative if there are hardware limitations during production.

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A Web-accessible distributed data warehouse for brain tumour diagnosis

The Knowledge Engineering Review ◽

10.1017/s0269888911000142 ◽

2011 ◽

Vol 26 (3) ◽

pp. 329-351 ◽

Cited By ~ 3

Author(s):

Francesc Estanyol ◽

Xavier Rafael ◽

Roman Roset ◽

Miguel Lurgi ◽

Mariola Mier ◽

...

Keyword(s):

Magnetic Resonance ◽

Brain Tumour ◽

Query Language ◽

Biological Data ◽

Management Tool ◽

Resonance Spectroscopy ◽

Tumour Diagnosis ◽

Wide Range ◽

The World ◽

Brain Tumour Diagnosis

AbstractCurrently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.

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KIKI ‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images

Magnetic Resonance in Medicine ◽

10.1002/mrm.27201 ◽

2018 ◽

Vol 80 (5) ◽

pp. 2188-2201 ◽

Cited By ~ 61

Author(s):

Taejoon Eo ◽

Yohan Jun ◽

Taeseong Kim ◽

Jinseong Jang ◽

Ho‐Joon Lee ◽

...

Keyword(s):

Neural Networks ◽

Magnetic Resonance ◽

Convolutional Neural Networks ◽

Magnetic Resonance Images ◽

Cross Domain

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The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy

The Knowledge Engineering Review ◽

10.1017/s0269888911000154 ◽

2011 ◽

Vol 26 (3) ◽

pp. 353-363 ◽

Cited By ~ 2

Author(s):

Alexander Gibb ◽

John Easton ◽

Nigel Davies ◽

YU Sun ◽

Lesley MacPherson ◽

...

Keyword(s):

User Interface ◽

Magnetic Resonance ◽

Magnetic Resonance Spectroscopy ◽

Brain Tumour ◽

Brain Tumours ◽

Resonance Spectroscopy ◽

Non Invasive ◽

Childhood Brain Tumours ◽

Tumour Diagnosis ◽

Brain Tumour Diagnosis

AbstractMagnetic resonance spectroscopy (MRS) is a non-invasive method, which can provide diagnostic information on children with brain tumours. The technique has not been widely used in clinical practice, partly because of the difficulty of developing robust classifiers from small patient numbers and the challenge of providing decision support systems (DSSs) acceptable to clinicians. This paper describes a participatory design approach in the development of an interactive clinical user interface, as part of a distributed DSS for the diagnosis and prognosis of brain tumours. In particular, we consider the clinical need and context of developing interactive elements for an interface that facilitates the classification of childhood brain tumours, for diagnostic purposes, as part of the HealthAgents European Union project. Previous MRS-based DSS tools have required little input from the clinician user and a raw spectrum is essentially processed to provide a diagnosis sometimes with an estimate of error. In childhood brain tumour diagnosis where there are small numbers of cases and a large number of potential diagnoses, this approach becomes intractable. The involvement of clinicians directly in the designing of the DSS for brain tumour diagnosis from MRS led to an alternative approach with the creation of a flexible DSS that, allows the clinician to input prior information to create the most relevant differential diagnosis for the DSS. This approach mirrors that which is currently taken by clinicians and removes many sources of potential error. The validity of this strategy was confirmed for a small cohort of children with cerebellar tumours by combining two diagnostic types, pilocytic astrocytomas (11 cases) and ependymomas (four cases) into a class of glial tumours which then had similar numbers to the other diagnostic type, medulloblastomas (18 cases). Principal component analysis followed by linear discriminant analysis on magnetic resonance spectral data gave a classification accuracy of 91% for a three-class classifier and 94% for a two-class classifier using a leave-one-out analysis. This DSS provides a flexible method for the clinician to use MRS for brain tumour diagnosis in children.

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Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images

IET Computer Vision ◽

10.1049/iet-cvi.2016.0482 ◽

2017 ◽

Vol 11 (8) ◽

pp. 643-649 ◽

Cited By ~ 10

Author(s):

Xulei Yang ◽

Zeng Zeng ◽

Su Yi

Keyword(s):

Neural Networks ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Left Ventricle ◽

Convolutional Neural Networks ◽

Automatic Segmentation ◽

Magnetic Resonance Images ◽

Deep Convolutional Neural Networks

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Liver Volumetry from Magnetic Resonance Images with Convolutional Neural Networks

10.1101/2021.12.01.21267152 ◽

2021 ◽

Author(s):

Sara L. Saunders ◽

Justin M. Clark ◽

Kyle Rudser ◽

Anil Chauhan ◽

Justin R. Ryder ◽

...

Keyword(s):

Neural Networks ◽

Magnetic Resonance ◽

Convolutional Neural Networks ◽

Liver Volume ◽

Network Models ◽

Magnetic Resonance Images ◽

Liver Fat ◽

Dice Similarity Coefficient ◽

Neural Network Models ◽

Mri Scans

AbstractPurposeTo determine which types of magnetic resonance images give the best performance when used to train convolutional neural networks for liver segmentation and volumetry.MethodsAbdominal MRI scans were performed on 42 adolescents with obesity. Scans included Dixon imaging (giving water, fat, and T2* images) and low-resolution T2-weighted anatomical scans. Multiple convolutional neural network models using a 3D U-Net architecture were trained with different input images. Whole-liver manual segmentations were used for reference.Segmentation performance was measured using the Dice similarity coefficient (DSC) and 95% Hausdorff distance. Liver volume accuracy was evaluated using bias, precision, and normalized root mean square error (NRMSE).ResultsThe models trained using both water and fat images performed best, giving DSC = 0.94 and NRMSE = 4.2%. Models trained without the water image as input all performed worse, including in participants with elevated liver fat. Models using the T2-weighted anatomical images underperformed the Dixon-based models, but provided acceptable performance (DSC ≥ 0.92, NMRSE ≤ 6.6%) for use in longitudinal pediatric obesity interventions.ConclusionThe model using Dixon water and fat images as input gave the best performance, with results comparable to inter-reader variability and state-of-the-art methods.

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