Automatic Segmentation of Left Atrial Geometry from Contrast-Enhanced Magnetic Resonance Images Using a Probabilistic Atlas

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2016.03.011 ◽

2016 ◽

Vol 72 ◽

pp. 90-107 ◽

Cited By ~ 15

Author(s):

Ceyda Nur Öztürk ◽

Songül Albayrak

Keyword(s):

Magnetic Resonance ◽

Knee Joint ◽

High Field ◽

Automatic Segmentation ◽

Region Growing ◽

Magnetic Resonance Images ◽

Voxel Classification

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Can the lessons learned from the assessment of automated electrocardiogram analysis in the Common Standards for quantitative Electrocardiography study benefit measurement of delayed contrast-enhanced magnetic resonance images?

Journal of Electrocardiology ◽

10.1016/j.jelectrocard.2007.02.010 ◽

2007 ◽

Vol 40 (3) ◽

pp. 246-250 ◽

Cited By ~ 5

Author(s):

Jocelyne Fayn ◽

Paul Rubel ◽

Peter W. Macfarlane

Keyword(s):

Magnetic Resonance ◽

Magnetic Resonance Images ◽

Lessons Learned ◽

Contrast Enhanced ◽

The Common

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Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks

Journal of Medical Imaging ◽

10.1117/1.jmi.6.1.011002 ◽

2018 ◽

Vol 6 (01) ◽

pp. 1 ◽

Cited By ~ 1

Keyword(s):

Magnetic Resonance ◽

Breast Lesion ◽

Short Term Memory ◽

Magnetic Resonance Images ◽

Short Term ◽

Term Memory ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced ◽

Long Short Term Memory ◽

Lesion Classification

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Fast Fully Automatic Segmentation of the Severely Abnormal Human Right Ventricle from Cardiovascular Magnetic Resonance Images Using a Multi-Scale 3D Convolutional Neural Network

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) ◽

10.1109/sitis.2016.16 ◽

2016 ◽

Cited By ~ 1

Author(s):

Archontis Giannakidis ◽

Konstantinos Kamnitsas ◽

Veronica Spadotto ◽

Jennifer Keegan ◽

Gillian Smith ◽

...

Keyword(s):

Neural Network ◽

Cardiovascular Magnetic Resonance ◽

Magnetic Resonance ◽

Right Ventricle ◽

Convolutional Neural Network ◽

Automatic Segmentation ◽

Magnetic Resonance Images ◽

Human Right ◽

Multi Scale ◽

Fully Automatic

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Automatic Segmentation of Left Atrial Scar from Delayed-Enhancement Magnetic Resonance Imaging

Functional Imaging and Modeling of the Heart - Lecture Notes in Computer Science ◽

10.1007/978-3-642-21028-0_8 ◽

2011 ◽

pp. 63-70 ◽

Cited By ~ 7

Author(s):

Rashed Karim ◽

Aruna Arujuna ◽

Alex Brazier ◽

Jaswinder Gill ◽

C. Aldo Rinaldi ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Left Atrial ◽

Automatic Segmentation ◽

Delayed Enhancement ◽

Resonance Imaging

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FLAIR2 improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images

NeuroImage Clinical ◽

10.1016/j.nicl.2019.101918 ◽

2019 ◽

Vol 23 ◽

pp. 101918

Author(s):

M. Le ◽

L.Y.W. Tang ◽

E. Hernández-Torres ◽

M. Jarrett ◽

T. Brosch ◽

...

Keyword(s):

Multiple Sclerosis ◽

Magnetic Resonance ◽

Automatic Segmentation ◽

Magnetic Resonance Images

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A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0988-4 ◽

2019 ◽

Vol 19 (S9) ◽

Cited By ~ 2

Author(s):

Vitoantonio Bevilacqua ◽

Antonio Brunetti ◽

Giacomo Donato Cascarano ◽

Andrea Guerriero ◽

Francesco Pesce ◽

...

Keyword(s):

Deep Learning ◽

Magnetic Resonance ◽

Kidney Disease ◽

Autosomal Dominant ◽

Automatic Segmentation ◽

Semantic Segmentation ◽

Magnetic Resonance Images ◽

Polycystic Kidney ◽

Autosomal Dominant Polycystic Kidney ◽

Segmentation Of Images

Abstract Background The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. Methods Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. Results Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. Conclusion The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

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