Segmentation based features for lymph node detection from 3-D chest CT

Feulner J, Zhou SK, Hammon M, Hornegger J, Comaniciu D (2011)

Publication Type: Book chapter / Article in edited volumes

Publication year: 2011

Journal

Lecture Notes in Computer Science Springer Verlag

Original Authors: Feulner J., Zhou S., Hammon M., Hornegger J., Comaniciu D.

Publisher: Springer-verlag

Edited Volumes: Machine Learning in Medical Imaging

Series: Lecture Notes in Computer Science

Book Volume: 7009

Pages Range: 91-99

Event location: Toronto, ON

Journal Issue: null

DOI: 10.1007/978-3-642-24319-6_12

Abstract

Lymph nodes routinely need to be considered in clinical practice in all kinds of oncological examinations. Automatic detection of lymph nodes from chest CT data is however challenging because of low contrast and clutter. Sliding window detectors using traditional features easily get confused by similar structures like muscles and vessels. It recently has been proposed to combine segmentation and detection to improve the detection performance. Features extracted from a segmentation that is initialized with a detection candidate can be used to train a classifier that decides whether the detection is a true or false positive. In this paper, the graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the candidate segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross validation on 54 CT datasets showed that the proposed system reaches a detection rate of 60.9% with only 6.1 false alarms per volume image, which is better than the current state of the art of mediastinal lymph node detection. © 2011 Springer-Verlag.

Authors with CRIS profile

Johannes Feulner Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME) Matthias Hammon Professur für Plastische Chirurgie und Handchirurgie Joachim Hornegger Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME)

Involved external institutions

Siemens Corporate Research US United States (USA) (US)

How to cite

APA:

Feulner, J., Zhou, S.K., Hammon, M., Hornegger, J., & Comaniciu, D. (2011). Segmentation based features for lymph node detection from 3-D chest CT. In Machine Learning in Medical Imaging. (pp. 91-99). Springer-verlag.

MLA:

Feulner, Johannes, et al. "Segmentation based features for lymph node detection from 3-D chest CT." Machine Learning in Medical Imaging. Springer-verlag, 2011. 91-99.

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