Right Ventricle Segmentation of Cine MRI Using Residual U-net Convolutinal Networks

Liu, Zexiong; Yang, Feng; Yang, Xuan

doi:10.1109/pdcat46702.2019.00072

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…At the same time, we also compare with the papers using various traditional expansion convolution (such as the convolution kernel is 3*3, 5*5, and 7*7) methods and the improved methods based on U‐net. For example, the classic medical image segmentation method U‐net, our inspiration paper Residual U‐net 34 . There are also methods that perform well in right ventricular segmentation data sets, such as CMIC, 6 SBIA, 7 Inception CNN, 35 Dilated CNN, 36 DIN, 33 and NTUST 8…”

Section: Resultsmentioning

confidence: 99%

“…For example, the classic medical image segmentation method U-net, our inspiration paper Residual U-net. 34 There are also methods that perform well in right ventricular segmentation data sets, such as CMIC, 6 SBIA, 7 Inception CNN, 35 Dilated CNN, 36 DIN, 33 and NTUST. 8 From Tables 5 and 6, we can see the superiority of U-snake's method.…”

Section: Comparison Of Segmentation Results With Other Methodsmentioning

confidence: 99%

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A U‐snake based deep learning network for right ventricle segmentation

Huang

Zhu

et al. 2022

Medical Physics

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Purpose Ventricular segmentation is of great importance for the heart condition monitoring. However, manual segmentation is time‐consuming, cumbersome, and subjective. Many segmentation methods perform poorly due to the complex structure and uncertain shape of the right ventricle, so we combine deep learning to achieve automatic segmentation. Method This paper proposed a method named U‐Snake network which is based on the improvement of deep snake together with level set to segment the right ventricular in the MR images. U‐snake aggregates the information of each receptive field which is learned by circular convolution of multiple dilation rates. At the same time, we also added dice loss functions and transferred the result of U‐Snake to the level set so as to further enhance the effect of small object segmentation. Our method is tested on the test 1 and test 2 datasets in the right ventricular segmentation challenge (RVSC), which shows the effectiveness. Results The experiment showed that we have obtained good result in the RVSC. The highest segmentation accuracy on the right ventricular test set 2 reached a dice coefficient of 0.911, and the segmentation speed reached 5 fps. Conclusions Our method, a new deep learning network named U‐snake, has surpassed the previous excellent ventricular segmentation method based on mathematical theory and other classical deep learning methods, such as Residual U‐net, Inception CNN, and Dilated CNN. However, it can only be used as an auxiliary tool instead of replacing the work of human beings.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison Of Segmentation Results With Other Methodsmentioning

confidence: 99%

A U‐snake based deep learning network for right ventricle segmentation

Huang

Zhu

et al. 2022

Medical Physics

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Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis

Åkesson

Ostenfeld

Carlsson

et al. 2023

Sci Rep

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Right ventricular (RV) volumes are commonly obtained through time-consuming manual delineations of cardiac magnetic resonance (CMR) images. Deep learning-based methods can generate RV delineations, but few studies have assessed their ability to accelerate clinical practice. Therefore, we aimed to develop a clinical pipeline for deep learning-based RV delineations and validate its ability to reduce the manual delineation time. Quality-controlled delineations in short-axis CMR scans from 1114 subjects were used for development. Time reduction was assessed by two observers using 50 additional clinical scans. Automated delineations were subjectively rated as (A) sufficient for clinical use, or as needing (B) minor or (C) major corrections. Times were measured for manual corrections of delineations rated as B or C, and for fully manual delineations on all 50 scans. Fifty-eight % of automated delineations were rated as A, 42% as B, and none as C. The average time was 6 min for a fully manual delineation, 2 s for an automated delineation, and 2 min for a minor correction, yielding a time reduction of 87%. The deep learning-based pipeline could substantially reduce the time needed to manually obtain clinically applicable delineations, indicating ability to yield right ventricular assessments faster than fully manual analysis in clinical practice. However, these results may not generalize to clinics using other RV delineation guidelines.

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Right Ventricle Segmentation of Cine MRI Using Residual U-net Convolutinal Networks

Cited by 2 publications

References 7 publications

A U‐snake based deep learning network for right ventricle segmentation

A U‐snake based deep learning network for right ventricle segmentation

Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis

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