Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images

Abstract

Objective: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions.

Materials and methods: A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation.

Results: In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). For the measurement of volumetric indices, the Bland-Altman 95% LOA was -0.17 ± 3.07% for liver volume and -0.56 ± 3.78% for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institution was comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation.

Conclusion: The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.

Keywords: Artificial intelligence; Deep learning; Liver; Segmentation; Spleen; Volumetry.

Fig. 1. Flow diagram of test datasets.

CLD = chronic liver disease, CT = computed tomography, OP = operative

Fig. 2. Schematic diagram of deep learning algorithm for liver and spleen segmentation.

Model receives three consecutive CT images as three-channel input using 2.5-dimensional input set-up and performs segmentation task on center section of CT images. Encoder part is based on modified Xception model, which contains series of downsampling layers and ASSPP unit. Output of encoder is feature maps, which are 32 × 32 × 728 in size. Decoder is series of bilinear upsampling layers with skip connections from encoder. Final output of model is three-channel (liver, spleen, and background) logit maps, which are same size as that of input CT image. ASSPP = Atrous Separable Spatial Pyramid Pooling, Conv = convolution

Fig. 3. Representative images showing deep learning-based liver and spleen segmentation results in various liver conditions.

Each row demonstrates original CT image, image of ground truth segmentation, image of deep learning segmentation, and image of segmentation error overlaid on CT image (red mask = false-positive segmentation; blue mask = false-negative segmentation). Images were obtained from healthy liver (first row), fatty liver disease (second row), liver cirrhosis (third row), and post-hepatectomy (fourth row) subgroups in test dataset-1.

Fig. 4. Bland-Altman plots for agreement between the liver volume (A), spleen volume (B), and liver/spleen volume ratio (C) measured using deep learning segmentation and those by ground truth segmentation.

Solid lines indicate mean differences and dashed lines indicate upper and lower limits of 95% limits of agreement. SD = standard deviation