Research Article: A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images

Date Published: June 18, 2019

Publisher: Public Library of Science

Author(s): Shanchen Pang, Tong Ding, Sibo Qiao, Fan Meng, Shuo Wang, Pibao Li, Xun Wang, Tao Song.


Locating diseases precisely from medical images, like ultrasonic and CT images, have been one of the most challenging problems in medical image analysis. In recent years, the vigorous development of deep learning models have greatly improved the accuracy in disease location on medical images. However, there are few artificial intelligent methods for identifying cholelithiasis and classifying gallstones on CT images, since no open source CT images dataset of cholelithiasis and gallstones is available for training the models and verifying their performance. In this paper, we build up the first medical image dataset of cholelithiasis by collecting 223846 CT images with gallstone of 1369 patients. With these CT images, a neural network is trained to “pick up” CT images of high quality as training set, and then a novel Yolo neural network, named Yolov3-arch neural network, is proposed to identify cholelithiasis and classify gallstones on CT images. Identification and classification accuracies are obtained by 10-fold cross-validations. It is obtained that our Yolov3-arch model is with average accuracy 92.7% in identifying granular gallstones and average accuracy 80.3% in identifying muddy gallstones. This achieves 3.5% and 8% improvements in identifying granular and muddy gallstones to general Yolo v3 model, respectively. Also, the average cholelithiasis identifying accuracy is improved to 86.50% from 80.75%. Meanwhile, our method can reduce the misdiagnosis rate of negative samples by the object detection model.

Partial Text

Cholelithiasis accompanying with gallstones is one of the most common and costly diseases in population, which is with an estimated prevalence of 10-20%. Statistically, symptomatic disease is responsible for 1.4 million visits and 750 000 cholecystectomies per year in the United States. More than 75% cholelithiasis patients belong to cholesterol or cholesterol-predominant type [1]. There are two fundamental features of cholelithiasis, composition and location. By chemical composition, gallstones can be separated into three classes: cholesterol-like gallstones, bile pigmented gallstones, and mixed gallstones. Considering the locations, there are three kinds of gallstones: gallbladder stones, intrahepatic bile duct stones, common hepatic bile duct stones. Identifying cholelithiasis and classifying the type of gallstones precisely on CT images is one of the most challenging problems in medical image analysis.

This research was allowed by Shandong Provincial Third Hospital and was carried out with the College of Computer and Communication Engineering, China University of Petroleum (East China), Shandong, China. The ethics committee has approved research and the college has reached a research agreement with the Shandong Provincial Third Hospital. All patients are anonymous and have no personal information.

It is shown in Fig 5 a correctly recognized negative sample. A negative sample is a CT image without gallstones. In the test set, we collect 96 CT images as negative samples. Through the correlation analysis of gallstones location information strategy, we change the confidence level of output and no image output bounding box.

In this paper, we collected more than 223846 CT images with gallstone of 1369 patients from The Third Hospital of Shandong Province, and then a data cleaning method is proposed to automatically select CT images in high quality. After that, we develop a deep learning method, Yolov3-arch neural network, for gallstones recognition. Using the method, the location of gallstones can be automatically marked, as well as the type can be identified. Experimental results show that our method can achieve accuracy 86.5% in recognizing both the type and location of gallstones, which performs better than classical Yolo neural networks. Meanwhile, our method can reduces the misdiagnosis rate of negative samples by the object detection model. According to the time of gallstones’ detection with our model, a patient’s CT images (about 200) may use approximately 4 seconds and it is 3-8 times faster than doctors are. In this way, we can save much time of doctors in searching gallstones.