Aggregated micropatch-based deep learning neural network for ultrasonic diagnosis of cirrhosis

Despite the advancements in the diagnosis of early-stage cirrhosis, the accuracy in the diagnosis using ultrasound is still challenging owing to the presence of various image artifacts, which results in poor visual quality of the textural and lower-frequency components. In this study, we propose an end-to-end multistep network called CirrhosisNet that includes two transfer-learned convolutional neural networks for semantic segmentation and classification tasks. It uses a uniquely designed image, called an aggregated micropatch (AMP), as an input image to the classification network, thereby assessing whether the liver is in a cirrhotic stage. With a prototype AMP image, we synthesized a bunch of AMP images while retaining the textural features. This synthesis significantly increases the number of insufficient cirrhosis-labeled images, thereby circumventing overfitting issues and optimizing network performance. Furthermore, the synthesized AMP images contained unique textural patterns, mostly generated on the boundaries between adjacent micropatches (μ-patches) during their aggregation. These newly created boundary patterns provide rich information regarding the texture features of the ultrasound image, thereby making cirrhosis diagnosis more accurate and sensitive. The experimental results demonstrated that our proposed AMP image synthesis is extremely effective in expanding the dataset of cirrhosis images, thus diagnosing liver cirrhosis with considerably high accuracy. We achieved an accuracy of 99.95 %, a sensitivity of 100 %, and a specificity of 99.9 % on the Samsung Medical Center dataset using 8 × 8 pixels-sized μ-patches. The proposed approach provides an effective solution to deep-learning models with limited-training data, such as medical imaging tasks.