Title :
Semi-Automatic Lymph Node Segmentation in LN-MRI
Author :
Unal, G. ; Slabaugh, Greg ; Ess, Andreas ; Yezzi, Anthony ; Fang, Tao ; Tyan, J. ; Requardt, M. ; Krieg, R. ; Seethamraju, R. ; Harisinghani, M. ; Weissleder, R.
Author_Institution :
Intelligent Vision & Reasoning, Siemens Corp. Res., Princeton, NJ, USA
Abstract :
Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences.
Keywords :
biological organs; biomedical MRI; cancer; data visualisation; image registration; image segmentation; image sequences; medical image processing; nanoparticles; surgery; tumours; 3D visualization; image registration; image segmentation; lymphatic dissemination; nanoparticle-enhanced lymphotropic magnetic resonance imaging; nodal cancer staging; post-contrast pelvic MRI sequences; prostate cancer; semiautomatic lymph node segmentation; surgical planning; Biomedical imaging; Cancer; Feature extraction; Image segmentation; Lymph nodes; Magnetic resonance imaging; Medical diagnostic imaging; Nanoparticles; Oncological surgery; Shape; biomedical image processing; biomedical magnetic resonance imaging; image segmentation; medical diagnosis;
Conference_Titel :
Image Processing, 2006 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
1-4244-0480-0
DOI :
10.1109/ICIP.2006.312366