Study on computer-aided diagnosis of hepatic MR imaging and mammography

Abstract

It is well known that the liver is an organ easily attacked by diseases. The purpose of this study is to develop a computer-aided diagnosis (CAD) scheme for helping radiologists to differentiate hepatic diseases more efficiently. Our software named LIVERANN integrated the magnetic resonance (MR) imaging findings with different pulse sequences to classify the five categories of hepatic diseases by using the artificial neural network (ANN) method. The intensity and homogeneity within the region of interest (ROI) delineated by a radiologist were automatically calculated to obtain numerical data by the program for input signals to the ANN. Outputs were the five pathological categories of hepatic diseases (hepatic cyst, hepatocellular carcinoma, dysplasia in cirrhosis, cavernous hemangioma, and metastasis). The experiment demonstrated a testing accuracy of 93% from 80 patients. In order to differentiate the cirrhosis from normal liver, the volume ratio of left to whole (LTW) was proposed to quantify the degree of cirrhosis by three-dimensional (3D) volume analysis. The liver region was firstly extracted from computed tomography (CT) or MR slices based on edge detection algorithms, and then separated into left lobe and right lobe by the hepatic umbilical fissure. The volume ratio of these two parts showed that the LTW ratio in the liver was significantly improved in the differentiation performance, with [formula omitted] in cirrhosis versus the normal liver [formula omitted]. In addition, the application of the ANN method for detecting clustered microcalcifications in masses on mammograms was described here as well. A new structural ANN, so-called a shift-invariant artificial neural network (SIANN), was integrated with our triple-ring filter (TRF) method in our CAD system. As the result, the sensitivity of detecting clusters was improved from 90% by our previous TRF method to 95% by using both SIANN and TRF. © 2005, American Association of Physicists in Medicine. All rights reserved.