Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm

AbstractAutomatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models.

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Segmentation of vestibular schwannoma from MRI - An open annotated dataset and baseline algorithm

10.1101/2021.08.04.21261588 ◽

2021 ◽

Author(s):

Jonathan Shapey ◽

Aaron Kujawa ◽

Reuben Dorent ◽

Guotai Wang ◽

Alexis Dimitriadis ◽

...

Keyword(s):

Automatic Segmentation ◽

Automated Segmentation ◽

Mr Images ◽

Clinical Workflow ◽

Open Source Framework ◽

Magnetic Resonance Imaging Mri ◽

Baseline Algorithm ◽

Gamma Knife Stereotactic Radiosurgery ◽

Development And Validation ◽

Human Annotator

Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models.

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A Robust Method for MR Image Segmentation and Multiple Scleroses Detection

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2019.2690 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1119-1130

Author(s):

H. Zouaoui ◽

A. Moussaoui ◽

M. Oussalah ◽

A. Taleb-Ahmed

Keyword(s):

White Matter ◽

Automatic Segmentation ◽

Fuzzy Model ◽

Mr Images ◽

New Approach ◽

Magnetic Resonance Imaging Mri ◽

Mr Image Segmentation ◽

The Brain

In the present article, we propose a new approach for the segmentation of the MR images of the Multiple Sclerosis (MS). The Magnetic Resonance Imaging (MRI) allows the visualization of the brain and it is widely used in the diagnosis and the follow-up of the patients suffering from MS. Aiming to automate a long and tedious process for the clinician, we propose the automatic segmentation of the MS lesions. Our algorithm of segmentation is composed of three stages: segmentation of the brain into regions using the algorithm Fuzzy Particle Swarm Optimization (FPSO) in order to obtain the characterization of the different healthy tissues (White matter, grey matter and cerebrospinal fluid (CSF)) after the extraction of white matter (WM), the elimination of the atypical data (outliers) of the white matter by the algorithm Fuzzy C-Means (FCM), finally, the use of a Mamdani-type fuzzy model to extract the MS lesions among all the absurd data.

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Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)

Diagnostics ◽

10.3390/diagnostics11091573 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1573 ◽

Cited By ~ 1

Author(s):

Nurin Syazwina Mohd Haniff ◽

Muhammad Khalis Abdul Karim ◽

Nurul Huda Osman ◽

M Iqbal Saripan ◽

Iza Nurzawani Che Isa ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Automatic Segmentation ◽

False Negative ◽

Features Extraction ◽

Manual Segmentation ◽

Resonance Imaging ◽

Mr Images ◽

3D Slicer ◽

Magnetic Resonance Imaging Mri ◽

Flood Fill

Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR images of HCC. Four types of features extraction were used for this study, which are tumour intensity, shape feature, textural feature and wavelet feature. A total of 662 radiomic features were extracted from manual and semi-automatic segmentation and compared using intra-class relation coefficient (ICC). Radiomic features extracted using semi-automatic segmentation utilized flood filling algorithm from 3D-slicer had significantly higher reproducibility (average ICC = 0.952 ± 0.009, p < 0.05) compared with features extracted from manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). Moreover, features extracted from semi-automatic segmentation were more robust compared to manual segmentation. This study shows that semi-automatic segmentation from 3D-Slicer is a better alternative to the manual segmentation, as they can produce more robust and reproducible radiomic features.

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An Algorithm Combining Analysis-based Blind Compressed Sensing and Nonlocal Low-rank Constraints for MRI Reconstruction

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666180130151333 ◽

2019 ◽

Vol 15 (3) ◽

pp. 281-291 ◽

Cited By ~ 1

Author(s):

Mei Sun ◽

Jinxu Tao ◽

Zhongfu Ye ◽

Bensheng Qiu ◽

Jinzhang Xu ◽

...

Keyword(s):

Compressed Sensing ◽

Signal Reconstruction ◽

Low Rank ◽

Mr Images ◽

Scanning Time ◽

Previous State ◽

Rank Approximation ◽

Mri Reconstruction ◽

Magnetic Resonance Imaging Mri ◽

Rank Constraints

Background: In order to overcome the limitation of long scanning time, compressive sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging (MRI) reconstruction. </P><P> Discussion: Blind compressed sensing enables to recover the image successfully from highly under- sampled measurements, because of the data-driven adaption of the unknown transform basis priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction with less time than synthesis-based blind compressed sensing. Recently, some experiments have shown that nonlocal low-rank property has the ability to preserve the details of the image for MRI reconstruction. Methods: Here, we focus on analysis-based blind compressed sensing, and combine it with additional nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation. </P><P> Results & Conclusion: Simulation results indicate that the proposed approach performs better than the previous state-of-the-art algorithms.

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A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

Scientific Reports ◽

10.1038/s41598-021-93227-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jared Hamwood ◽

Beat Schmutz ◽

Michael J. Collins ◽

Mark C. Allenby ◽

David Alonso-Caneiro

Keyword(s):

Deep Learning ◽

Human Performance ◽

Automatic Segmentation ◽

Ct Images ◽

Human Observer ◽

In Series ◽

Magnetic Resonance Imaging Mri ◽

High Degree ◽

Orbital Region ◽

Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

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