Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

THIS WEBPAGE IS DEPRECATED - please visit our new website

M. Sofka, K. Ralovich, N. Birkbeck, J. Zhang, K. Zhou
Integrated Detection Network (IDN) For Pose And Boundary Estimation In Medical Images
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2011), Chicago, Illinois, USA, March 30 - April 2, 2011 (bib)

The expanding role of complex object detection algorithms introduces a need for flexible architectures that simplify interfacing with machine learning techniques and offer easy-to-use training and detection procedures. To address this need, the Integrated Detection Network (IDN) proposes a conceptual design aimed at rapid prototyping of sophisticated systems for object and boundary detection. The IDN uses a strong spatial prior present in the medical imaging domain and a large annotated database of images to train robust detectors. The best detection hypotheses are propagated throughout the detection network using sequential sampling techniques. The effectiveness of the IDN is demonstrated on two learning-based algorithms: (1) automatic detection of fetal brain structures in ultrasound volumes, and (2) liver boundary detection in MRI volumes. Modifying the detection pipeline in both cases is simple and allows for immediate adaptation to the variations of the desired algorithm. Both systems achieved low detection error (3.09 and 4.20 mm for two brain structures and 2.53 mm for boundary).
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 30 Jan 2019 - 15:16 - LeslieCasas

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif