Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey

Abstract

Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine "optimal" boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.

Keywords: CT colonography; classification; colorectal polyp; computer-aided diagnosis; lung nodule; machine learning in medical imaging; pixel-based machine learning.

Fig. 1

Feature-based ML (feature-based classifier) for classification of a detected and segmented lesion.

Fig. 2

Architecture of an MTANN (a class of PML) consisting of an ML regression model (e.g., linear-output ANN regression and support-vector regression) with sub-region (local window or patch) input and single-pixel output.