Patient-specific models for lung nodule detection and surveillance in CT images

Abstract

The purpose of this work is to develop patient-specific models for automatically detecting lung nodules in computed tomography (CT) images. It is motivated by significant developments in CT scanner technology and the burden that lung cancer screening and surveillance imposes on radiologists. We propose a new method that uses a patient's baseline image data to assist in the segmentation of subsequent images so that changes in size and/or shape of nodules can be measured automatically. The system uses a generic, a priori model to detect candidate nodules on the baseline scan of a previously unseen patient. A user then confirms or rejects nodule candidates to establish baseline results. For analysis of follow-up scans of that particular patient, a patient-specific model is derived from these baseline results. This model describes expected features (location, volume and shape) of previously segmented nodules so that the system can relocalize them automatically on follow-up. On the baseline scans of 17 subjects, a radiologist identified a total of 36 nodules, of which 31 (86%) were detected automatically by the system with an average of 11 false positives (FPs) per case. In follow-up scans 27 of the 31 nodules were still present and, using patient-specific models, 22 (81%) were correctly relocalized by the system. The system automatically detected 16 out of a possible 20 (80%) of new nodules on follow-up scans with ten FPs per case.

Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

“…CAD schemes for lung nodule detection were developed first for chest radiographs [6] and then for thick-section CT images [7][8][9][10][11][12][13]. The typical performance of current CAD schemes in thick-section CT is an 80-90% sensitivity with 1-2 false positives per section, which translates into tens of false positives per CT scan.…”

Computational methods for the analysis of functional 4D-CT chest images.

“…Then, a rule based filter was used to combine these features in order to detect the lung nodules. Brown et al [109] build semantic network priori models to describe the lung nodules and other structures. In the training phase, a set of features, composed of the X-ray attenuation range, the relative location, the volume, and a sphericity sphericity shape parameter, were used in the semantic network nodes to describe the expectation models for clustering strategy combined with a marching cube algorithm from a sphere based shape.…”

“…The drawbacks to these approaches are the difficulties in detecting lung wall nodules. Also, there are other pattern recognition techniques used in detection of lung nodules such as clustering [100][101][102][103], linear discriminate functions [104], rule-based classification [105], Hough transform [106], connected component analysis of thresholded CT slices [107,108], gray level distance transform [102], and patient-specific a priori model [109]. The FPNs are excluded at the second stage by nodule classification [82,83,106,[110][111][112].…”

“…The task of the classifier is to determine "optimal" boundaries for separating classes (i.e., nodules or non-nodules) in the multi-dimensional feature space which is formed by the input features [113]. Featurebased classifiers include linear discriminant analysis (LDA) [114], rule-based or linear clas-sifier [38,85,86,88,90,100,103,115]; template matching [109]; nearest cluster [97,99];…”

“…Accurate nodule segmentation is a crucial prerequisite for various diagnostic and treatment procedures for lung cancer [166], such as diagnosing tumor growth in followup CTs [167,168], monitoring tumor response to therapy [169,170], computer-aided lung cancer screening for early detection [93,109,171], and computer-aided diagnosis of tumor malignancy [137,172]. In this application context, segmentation accuracy directly influences important clinical factors, such as the minimum size of measurable lesions and the shortest time duration for repeat CT in follow-up studies.…”