Segmentation of Lesions with Improved Specificity in Computer-Aided Diagnosis Using a Massive-Training Artificial Neural Network (MTANN)

Segmentation of lesions plays an important role in computer-aided diagnostic (CAD) schemes, because the accuracy of segmentation affects the accuracy of the feature extraction and analysis based on segmented lesions, and therefore, the final accuracy of classification. Accurate segmentation is difficult especially for complicated patterns such as lesions overlapping or touching normal structures, low-contrast lesions, and subtle opacities. With standard segmentation methods, normal structures overlapping or touching lesions are often erroneously included in segmented regions. In addition, normal structures are often segmented erroneously as lesions. Thus, improving the specificity of segmentation methods is very important in the development of a CAD scheme. Our purpose in this study was to develop a supervised lesion segmentation method based on a massive-training artificial neural network (MTANN) filter in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to segment nodules with improved specificity. With the MTANN-based segmentation method, the specificity of the segmentation was improved; thus, the overall performance of our CAD scheme was improved substantially.