Luca Bogoni PhD, Abstract Co-Author: Employee, Siemens AG, Malvern, PA

Matthias Wolf, Abstract Co-Author: Researcher, Siemens AG, Malvern, PA

Adrian Barbu PhD, Abstract Co-Author: Employee, Siemens AG

Sarang Lakare, Abstract Co-Author: Researcher, Siemens AG, Malvern, PA

Murat Dundar PhD, Abstract Co-Author: Researcher, Siemens AG, Malvern, PA

Marcos Salganicoff PhD, Abstract Co-Author: Employee, Siemens AG

Le Lu PhD, Presenter: Employee, Siemens AG

Evaluate the performance of a module for suppression of false positive CAD marks located on the ileo cecal valve (ICV) in a prototype polyp detection system when applied to clean or tagged CTC datasets

The ICV detection component uses 5 steps: a) detecting the ICV orifice leveraging its distinctive local curvature profile, b) a rough estimation of its orientation by aligning it with the local gradients at a given location. Estimation of the c) position, d) size of the ICV and e) refining the orientation is calculated by performing marginal space learning. In each of the 5 steps, all potential orifice candidates are evaluated using different classifiers. The top 100 candidates with maximal orifice probabilities are selected for further parameter estimation and searching. The number of selected candidates is set to maintain a good trade-off between detection accuracy and speed. The position of 116 manually marked ICV orifices were used to generate a training set of 8,360 positive and 3,700,000 negative samples being used to train a probabilistic boosting tree. On the training set a sensitivity of 97.4% (113/116) was achieved. The processing time varied from 4 to 10 seconds on a P4 3.2GHz computer

The ICV detection was tested on 802 unseen datasets: 407 clean volumes from 10 different sites; 395 tagged volumes, including iodine and barium preparations, from 2 sites. The ICV detection was implemented as post filter. In clean cases, ICV detection reduced the number of false positives (fp) from 3.92 fp/pat (2.04 fp/vol.) to 3.72 fp/pat (1.92 fp/vol.) without impacting the overall sensitivity of the CAD system. In tagged cases ICV detection reduced the number of false marks from 6.2 fp/pat (3.15 fp/vol.) to 5.78 fp/pat (2.94 fp/vol.). 1 polyp out of 121 polyps with a size range from 6 up to 25 mm was wrongly labeled as ICV, resulting in a sensitivity drop of 0.8%.

ICV detection effectively reduces detection false positives, both in clean and tagged cases, from the list of marks presented to the reader by ColonCAD without impacting the system sensitivity

Ileo cecal valve detection and false positive reduction improve the performance and acceptance of CAD systems for CTC

Bogoni, L, Wolf, M, Barbu, A, Lakare, S, Dundar, M, Salganicoff, M, Lu, L, Learning-based Component for Suppression of False Positives Located on the Ileo Cecal Valve: Evaluation of Performance on 802 CTC Volumes.  Radiological Society of North America 2007 Scientific Assembly and Annual Meeting, November 25 - November 30, 2007 ,Chicago IL. http://archive.rsna.org/2007/5013312.html