A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection

Abstract

Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD).

Materials and methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated.

Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001).

Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

Keywords: Aortic dissection; Deep learning; Tomography, X-ray computed.

Fig. 1. Overview of the applied framework.

CTA = computed tomography angiography, FL = false lumen, IoU = intersection over union, TL = true lumen, 3D = three-dimensional

Fig. 2. Framework of convolutional neural network.

D = depth, H = height, W = width

Fig. 3. Automatic segmentation results with the DL method.

A. Four levels (a, b, c, d) of ground truth and DL segmentation of aorta. B. DCSs for the Ascending, Arch, DA, and AA in the test set. C. DCSs for the entire aorta, TL and FL in the test set. (B, C) are the corresponding mean and error bar plot, displaying the distribution of DCS in test set based on minimum, mean, and maximum values. AA = abdominal aorta, Arch = aortic arch, Ascending = ascending aorta, DA = descending aorta, DCS = dice coefficient score, DL = deep learning

Fig. 4. Linear relationships between reference and measurements from manual and DL methods.

A. Correlation between measurements from manual method and reference standard (p < 0.001, r = 0.964, R² = 0.759). B. Correlation between measurements from DL method and reference standard (p < 0.001, r = 0.991, R² = 0.911). The correlation between measurements from DL method and reference standard was better than that from manual method. R = Pearson's correlation coefficient, R² = coefficient of determination

Fig. 5. Measurement performance for the entire aorta, TL, and FL in 25 cases with DL and manual methods.

A. Measurement error for the entire aorta at eight levels. B. Measurement error for the TL at four levels. C. Measurement error for the FL at four levels. In the box plots, the boxes indicate the median and interquartile range, while the error bars represent the minimum and maximum values. Significance is labeled as follows: ^*p < 0.05, ^**p < 0.01. P1 = innominate artery origin, P2 = proximal to the left common carotid artery origin, P3 = proximal to the left subclavian artery origin, P4 = distal to the left subclavian artery origin, P5 = at the level of the diaphragm, P6 = at the superior border of the celiac axis origin, P7 = at the superior border of the lower renal aorta origin, P8 = proximal to the aortic bifurcation

Fig. 6. Bland-Altman plots for diameter measurements.

Bland-Altman plots for the entire aorta (A, B), TL (C, D), and FL (E, F) for deviations of diameters between manual method and reference standard (A, C, E), as well as DL method and reference standard (B, D, F) at each measurement position in the 25 cases.