Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI

Curiale, Ariel Hernán; Colavecchia, F. D.; Mato, Germán

doi:10.1016/j.cmpb.2018.12.002

Cited by 26 publications

(15 citation statements)

References 32 publications

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“…These hyperparameters obtained the best performance in both networks. These values match those described in similar neural networks for the same tasks [9,10,11].…”

Section: Network Trainingsupporting

confidence: 82%

PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles

Pérez-Pelegrí

Monmeneu²,

López‐Lereu³

et al. 2020

2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)

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Characterization of the heart anatomy and function is mostly done with magnetic resonance image cine series. To achieve a correct characterization, the volume of the right and left ventricle need to be segmented, which is a timeconsuming task. We propose a new convolutional neural network architecture that combines U-net with PSP modules (PSPU-net) for the segmentation of left and right ventricle cavities and left ventricle myocardium in the diastolic frame of short-axis cine MRI images and compare its results against a classic 3D U-net architecture. We used a dataset containing 399 cases in total. The results showed higher quality results in both segmentation and final volume estimation for a test set of 99 cases in the case of the PSPU-net, with global dice metrics of 0.910 and median absolute relative errors in volume estimations of 0.026 and 0.039 for the left ventricle cavity and myocardium and 0.051 for the right ventricles cavity.

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“…These hyperparameters obtained the best performance in both networks. These values match those described in similar neural networks for the same tasks [9,10,11].…”

Section: Network Trainingsupporting

confidence: 82%

PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles

Pérez-Pelegrí

Monmeneu²,

López‐Lereu³

et al. 2020

2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)

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“…Inception modules of different configurations have been applied on a multitude of U-net applications, including brain tumor detection [19], [44], [45], brain tissue mapping [46], cardiac segmentation [7], [47], lung nodule detection [48], human embryo segmentation [49], and ultrasound nerve segmentation [50].…”

Section: Inception U-netmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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“…Three-dimensional imaging of the cardiovascular system exhibits a broader range of imaging modalities, including CT and MRI. Lung and pulmonary structures were segmented using the UNet on CT scans [ 9 , 16 , 17 , 18 ] and cardiovascular structures with MRI [ 19 , 20 , 21 , 22 ]. Three-dimensional UNet segmentation has also been used for segmentation of liver tumors in CT scans [ 23 , 24 ] and MRI [ 25 ], prostate and breast cancer in MRI [ 26 , 27 ], multi-organ segmentation from CT [ 28 , 29 , 30 ], and osteosarcoma [ 31 ] and vertebrae [ 32 ] from CT.…”

Section: Introductionmentioning

confidence: 99%

Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation

Jiang

Chitrakar

et al. 2021

Sensors

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Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation.

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Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI

Cited by 26 publications

References 32 publications

PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles

PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation

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