A Robust and Fast System for CTC Computer-Aided Detection of Colorectal Lesions

Abstract

We present a complete, end-to-end computer-aided detection (CAD) system for identifying lesions in the colon, imaged with computed tomography (CT). This system includes facilities for colon segmentation, candidate generation, feature analysis, and classification. The algorithms have been designed to offer robust performance to variation in image data and patient preparation. By utilizing efficient 2D and 3D processing, software optimizations, multi-threading, feature selection, and an optimized cascade classifier, the CAD system quickly determines a set of detection marks. The colon CAD system has been validated on the largest set of data to date, and demonstrates excellent performance, in terms of its high sensitivity, low false positive rate, and computational efficiency

Sparse Classification for Computer Aided Diagnosis Using Learned Dictionaries

“…Our datasets are representative, but very challenging with large within-class variations for polyp, nodule class and other anatomical structures in colon and lung volumes. The results validate that this new classification framework can significantly improve the accuracy of our baseline computer-aided detection system, using the same set of input image features, and compare favorably with other state-of-the-arts [1][2][3][4][6][7][8].…”

“…more than 100 candidates per scan with one to two true positives. Then dozens or hundreds of heterogeneous image features can be computed per VOI, in domains of volumetric shape, intensity, gradient, texture and even context [1,3,4,7,8]. Lastly, the essential goal for classification is achieving the best balance between high sensitivities and low false positive rates, given VOIs and associated features.…”

“…However, these two cancers are highly preventable or "curable" if detected early. Image interpretation based cancer detection via 3D computer tomography has emerged as a common clinical practice, and many computer-aided detection tools for enhancing radiologists' diagnostic performance and effectiveness are developed in the last decade [1][2][3][4][6][7][8]. The key for radiologists to accept the clinical usage of a CAD system is highest possible detection sensitivity with reasonably low false positive (FP) rate per case.…”

A Bayesian Approach for False Positive Reduction in CTC CAD

“…The proposed method has been applied to the candidate regions found by our previous CAD algorithm [6]. Quantitative evaluation on a large multi-center clinical dataset of colonic CT scans shows the excellent performance of the method, which reduces the FPs by an average 16%, while keeping the same sensitivity.…”

Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model

“…As such, the rectal tube is a common source of false positives generated by the CAD for CT Colonography (CTC) [1][2][3][4]. To improve the overall CAD performance, it is therefore desirable to have a robust and efficient way to identify the RT and remove its resulting FPs from the CAD marks presented to the reader.…”