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Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per(More)
PURPOSE To apply a computer-aided detection (CAD) algorithm to supine and prone multisection helical computed tomographic (CT) colonographic images to confirm if there is any added benefit provided by CAD over that of standard clinical interpretation. MATERIALS AND METHODS CT colonography (with patients in both supine and prone positions) was performed(More)
An automatic method to segment colonic polyps in computed tomography (CT) colonography is presented in this paper. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy c-mean clustering, and deformable models. The computer segmentations were compared with manual segmentations to validate the accuracy of our method. An average(More)
Automated detection of sclerotic metastases (bone lesions) in Computed To-mography (CT) images has potential to be an important tool in clinical practice and research. State-of-the-art methods show performance of 79% sensitivity or true-positive (TP) rate, at 10 false-positives (FP) per volume. We design a two-tiered coarse-to-fine cascade framework to(More)
Virtual colonoscopy (VC) is becoming a more prevalent method to detect and diagnose colorectal cancer. An essential component of using VC to detect cancerous polyps, especially in conjunction with computer-aided diagnosis, is the accurate calculation of the centerline of the colon. While the colon is often modeled as a simple cylinder, the amount of colonic(More)
We present a new method for guiding virtual colonoscopic navigation and registration by using teniae coli as anatomical landmarks. As most existing protocols require a patient to be scanned in both supine and prone positions to increase sensitivity in detecting colonic polyps, reference and registration between scans are necessary. However, the conventional(More)
Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities  ∼ 100% of but at(More)
The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four(More)