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Technical Program
Paper Detail
| Paper: | MA-L3.4 |
| Session: | Biomedical Image Segmentation |
| Time: | Monday, October 9, 10:40 - 11:00 |
| Presentation: |
Lecture
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| Title: |
SEMI-AUTOMATIC LYMPH NODE SEGMENTATION IN LN-MRI |
| Authors: |
Gozde Unal; Siemens Corporate Research, Inc. | | | | | Greg Slabaugh; Siemens Corporate Research, Inc. | | | | | Andreas Ess; Swiss Federal Institute of Technology | | | | | Anthony Yezzi; Georgia Institute of Technology | | | | | Tong Fang; Siemens Corporate Research, Inc. | | | | | Jason Tyan; Siemens Corporate Research, Inc. | | | | | Martin Requardt; Siemens Corporate Research, Inc. | | | | | Robert Krieg; Siemens Corporate Research, Inc. | | | | | Ravi Seethamraju; Siemens Corporate Research, Inc. | | | | | Mukesh Harisinghani; Massachusetts General Hospital | | | | | Ralph Weissleder; Massachusetts General Hospital | | |
| 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. |
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