Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

Abstract

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis

“…However, it does suggest the research question of whether the mapping between image features and semantic concepts can be improved if there were an enhanced lexicon for describing the whole nodule that is not sensitive to segmentation boundary as proposed by El-Baz et al [22]. A lexicon that describes the whole nodule may allow for providing different descriptive semantics for nodule margin for different size domains.…”

“…The survey article by El-Baz et al provides an excellent review of these efforts [22]. In many cases, these researchers demonstrated CAD systems that use novel image features that are applicable to the study of spiculation.…”

“…Recently, researchers have identified the need to develop qualitative measures of nodule appearance that have the ability to describe the whole shape and appearance of the nodule, because the existing set of shape and appearance features depend Fig. 1 Example of a nodule outline and its annotation variability in the LIDC on the quality of the segmentation algorithm [22]. This research contributes to this endeavor by better defining the limits where existing image features map well to existing semantic concepts of shape.…”

Automated pathological growths parametrization based on computed tomography layer segmentation

“…They can simplify CT scan reading process, but can't lead to automatic parameter evaluation. Also, there are some methods that analyze CT layers separately, for example pulmonary nodes classification methods [8,9,10]. This kind of methods is suitable for object classification problems, which can be solved with the use of neural networks [11].…”

Improve the diagnosis of atrial hypertrophy with the local discriminative support vector machine

“…In computer science community, CAD approach belongs to pattern recognition or machine learning, with the aim to let computer yield a second opinion for medical staff. Although CAD approaches behave well in detecting many cardiovascular diseases [5][6][7], they are not good at addressing atrial hypertrophy disease. The reason is that the training data is extremely rare.…”