Abstract
Due to the fact that previous studies have rarely investigated the recognition rate discrepancy and pathology data error when applied to different databases, the purpose of this study is to investigate the improvement of recognition rate via deep learning-based liver lesion segmentation with the incorporation of hospital data. The recognition model used in this study is H-DenseUNet, which is applied to the segmentation of the liver and lesions, and a mixture of 2D/3D Hybrid-DenseUNet is used to reduce the recognition time and system memory requirements. Differences in recognition results were determined by comparing the training files of the standard LiTS competition data set with the training set after mixing in an additional 30 patients. The average error value of 9.6% was obtained by comparing the data discrepancy between the actual pathology data and the pathology data after the analysis of the identified images imported from Kaohsiung Chang Gung Memorial Hospital. The average error rate of the recognition output after mixing the LiTS database with hospital data for training was 1%. In the recognition part, the Dice coefficient was 0.52 after training 50 epochs using the standard LiTS database, while the Dice coefficient was increased to 0.61 after adding 30 hospital data to the training. After importing 3D Slice and ITK-Snap software, a 3D image of the lesion and liver segmentation can be developed. It is hoped that this method could be used to stimulate more research in addition to the general public standard database in the future, as well as to study the applicability of hospital data and improve the generality of the database.
Highlights
IntroductionChronic liver disease and liver cancer are among the leading causes of death each year
It is remarkable that the average error of identification and pathological anatomy data using the LiTS database was 9.6%, while the average error was reduced to 1% with the addition of hospital data
According to the current trend of increasing number of studies on the application of deep learning to medical image recognition and increasing recognition rate, the application of deep learning results to medical centers or to patients for rapid detection and classification of conditions can be seen in the near future, and its recognition capability can surpass the visual observation of radiologists
Summary
Chronic liver disease and liver cancer are among the leading causes of death each year. Hepatocellular carcinoma (HCC) is mainly caused by primary malignant tumors of the liver and is commonly seen in patients with chronic liver disease or cirrhosis following hepatitis B and C infection and has fewer early symptoms. Radiologists often need to collect a lot of patient information and review each magnetic resonance imaging (MRI) report separately, which generates a huge workload and may delay the optimal treatment time for liver cancer patients. Artificial intelligence (AI) has been able to predict liver lesions as well as avoid its deterioration
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