Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver

Abstract

Purpose: Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD.

Materials and methods: The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland-Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC.

Results: The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry.

Conclusions: PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.

Keywords: Artificial intelligence; Polycystic kidney diseases; Tomography.

Fig. 1. Sequential experiments to evaluate the performance of our framework for automatic segmentation and volumetry. (A) The first phase illustrates the process of multiorgan segmentation. The volumetric performance of our framework on 39 CT scans (3,302 image slices) in the validation set is analyzed in Fig. 3. (B) In the second phase of the experiment, the performance of our framework on 50 randomly selected PKLD image slices was compared to that of 11 PKLD experts. The results of the comparative analysis are illustrated in Fig. 3 and Fig. 4. CT, computed tomography; GT, ground-truth; ICC, interobserver correlation coefficient; AI, artificial intelligence; PKLD, polycystic kidney and liver disease.

Fig. 2. Performance evaluation for the volume calculations based on automatic segmentation using our framework. (A) Interobserver correlation coefficients, (B) Bland–Altman analysis, and (C) levels of acceptability classified as level A (perfectly acceptable), B (acceptable), C (slightly acceptable), and D (unacceptable). ICC, interobserver correlation coefficient; GT, ground-truth.

Fig. 3. The comparative analysis of performance between our framework and specialists. (A) Performance on the second-phase experiment of the independent test set of 50 randomly selected CT image slices. The receiver operating characteristic (ROC) diagram shows the segmentation accuracy of our framework versus all 11 experts. The blue ROC curve was created by sweeping a threshold over the inference of our framework for the ground-truth. (B) Table presenting the results of the performance comparison, the time spent for 50 image slices, the processable number of image slices in 1 hour, and the clinical experience of each specialist. N01-06, nephrologists; R01–04, radiologists; U01, urologist; PCK, polycystic kidney disease.

Fig. 4. Comparison of segmentation performance with that of human experts. (A) The details of the dice similarity coefficient (lower left, red) and the interobserver correlation coefficient (right upper, blue) are listed for each specialist. (B) Heatmap table for clearer comparisons and visibility. GT, ground-truth; AI, artificial intelligence; N01-06, nephrologists; R01-04, radiologists; U01, urologist; ICC, interobserver correlation coefficient.

Fig. 5. Schematic diagram of the V-net architecture of our framework. Our custom implementation processes three-dimensional data by performing volumetric convolutions. Conv., convolutional.