Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net

Abstract

Cardiac magnetic resonance (CMR) imaging is considered the standard imaging modality for volumetric analysis of the right ventricle (RV), an especially important practice in the evaluation of heart structure and function in patients with repaired Tetralogy of Fallot (rTOF). In clinical practice, however, this requires time-consuming manual delineation of the RV endocardium in multiple 2-dimensional (2D) slices at multiple phases of the cardiac cycle. In this work, we employed a U-Net based 2D convolutional neural network (CNN) classifier in the fully automatic segmentation of the RV blood pool. Our dataset was comprised of 5,729 short-axis cine CMR slices taken from 100 individuals with rTOF. Training of our CNN model was performed on images from 50 individuals while validation was performed on images from 10 individuals. Segmentation results were evaluated by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Use of the CNN model on our testing group of 40 individuals yielded a median DSC of 90% and a median 95 th percentile HD of 5.1 mm, demonstrating good performance in these metrics when compared to literature results. Our preliminary results suggest that our deep learning-based method can be effective in automating RV segmentation.

Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network

“…Thus, we believe that accumulation of slight errors on the right side of the heart resulted in a weak correlation of the FAC on the right side in our study. Similar to that reported previously, the low extraction accuracy of the right side of the heart remains a major challenge for automated segmentation based on U-Net CNN, and further work is required in this field [17,[26][27][28]. Nevertheless, we demonstrated that U-Net CNN enables extraction of areas in all the four cardiac chambers, with high accuracy, using 4CH cine CMR images.…”

“…Each image predicted by the developed U-Net CNN contained blood pools that do not correspond to each cardiac chamber. To correct the morphology, the predicted images underwent morphological closing using a 3-pixel radius disk, based on selection of the largest area and as reported previously [17]. The closed images were then binarized by the adapted threshold method [21].…”

“…Moreover, observer-dependent errors owing to the subjectivity of the analysis using semiautomatic feature-tracking measurements cannot be eliminated. To solve these problems, highly accurate automatic extraction of the LV and RV using deep learning for short-axis cine CMR images has been reported [16,17]. However, to the best of our knowledge, there are no reports of automatic extraction of cardiac chambers using 4CH cine CMR.…”

Pediatric Cardiac Magnetic Resonance Reference Values for Biventricular Volumes Derived From Different Contouring Techniques

“…19,20 This has potential to improve clinical workflow, but importantly it is based on volumetric analysis which includes papillary muscles and trabeculations in the ventricular volumes. 20,21 Linear regression analysis showed significant associations of LV and RV volumetric parameters with studied parameters (gender, age, BSA, body weight, and body height). Subsequent multivariable regression analysis demonstrated that body height and body weight showed significant associations with volumetric results.…”

Compare the Segmentation Accuracy of Heart Substructure on Contrast Enhanced CT by Deep Convolutional Neural Network with Different Loss Functions

“…At present, there have been many researches on automatic heart segmentation, but most of them are carried out on high-quality and well-displayed images such as MRI and CTA (39)(40)(41)(42).This research is carried out on radiotherapy positioning CT, which is the basic image of clinical radiotherapy. The image quality is relatively low and it is difficult to identify the border of the organ.We combined different loss functions into different deep neural networks, and compared their segmentation effects.Tran (43) et al used U-Net-based CNN to segment the right ventricle on cardiac MRI, and obtained an average DSC of 0.9 (95%HD: 5.1mm). In this study, GDL U-Net segmented the right ventricle with an average DSC of 0.912 (95% HD: 4.242 mm), which is relatively better than Tran's segmentation results.In addition, most of the current researches on the automatic segmentation of the heart are single-organ segmentation, and only a few researches on the automatic segmentation of the heart and two ventricles.…”