Automatische Detektion von Lymphknoten in CT-Datensätzen des Halses

Dornheim, Lars; Dornheim, Jana

doi:10.1007/978-3-540-78640-5_62

Cited by 11 publications

(17 citation statements)

References 6 publications

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“…There is a limited amount of work directed to automatic lymph node detection [4,5,7]. These works target mediastinal [5], abdominal [7] and neck [4] lymph nodes while our work targets axillary lymph nodes.…”

Section: Fig 1 Diagram Of the Axillary Lymph Node Detection Systemmentioning

confidence: 99%

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Automatic Detection and Segmentation of Axillary Lymph Nodes

Barbu

Suehling

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. Lymph node detection and measurement is a difficult and important part of cancer treatment. In this paper we present a robust and effective learning-based method for the automatic detection of solid lymph nodes from Computed Tomography data. The contributions of the paper are the following. First, it presents a learning based approach to lymph node detection based on Marginal Space Learning. Second, it presents an efficient MRF-based segmentation method for solid lymph nodes. Third, it presents two new sets of features, one set self-aligning to the local gradients and another set based on the segmentation result. An extensive evaluation on 101 volumes containing 362 lymph nodes shows that this method obtains a 82.3% detection rate at 1 false positive per volume, with an average running time of 5-20 seconds per volume.

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Section: Fig 1 Diagram Of the Axillary Lymph Node Detection Systemmentioning

confidence: 99%

“…These works target mediastinal [5], abdominal [7] and neck [4] lymph nodes while our work targets axillary lymph nodes. The axillary lymph nodes are far from airways or major vessels, so a segmentation of the vessels or airways is not necessary.…”

Section: Fig 1 Diagram Of the Axillary Lymph Node Detection Systemmentioning

confidence: 99%

Automatic Detection and Segmentation of Axillary Lymph Nodes

Barbu

Suehling

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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show abstract

“…To our knowledge, so far only three approaches [9][10][11] to (semi-)automatic lymph node detection in CT datasets were presented. Before Eicke 9 could start an automatic extraction process of neck lymph nodes by template matching in the Fourier space, he manually adjusted the lymph node intensity (HU) range for each experimental dataset by means of a previously obtained gold standard segmentation.…”

Section: Introductionmentioning

confidence: 99%

Automatic mediastinal lymph node detection in chest CT

Feuerstein

Deguchi

Kitasaka

et al. 2009

Medical Imaging 2009: Computer-Aided Diagnosis

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Computed tomography (CT) of the chest is a very common staging investigation for the assessment of mediastinal, hilar, and intrapulmonary lymph nodes in the context of lung cancer. In the current clinical workflow, the detection and assessment of lymph nodes is usually performed manually, which can be error-prone and timeconsuming. We therefore propose a method for the automatic detection of mediastinal, hilar, and intrapulmonary lymph node candidates in contrast-enhanced chest CT. Based on the segmentation of important mediastinal anatomy (bronchial tree, aortic arch) and making use of anatomical knowledge, we utilize Hessian eigenvalues to detect lymph node candidates. As lymph nodes can be characterized as blob-like structures of varying size and shape within a specific intensity interval, we can utilize these characteristics to reduce the number of false positive candidates significantly. We applied our method to 5 cases suspected to have lung cancer. The processing time of our algorithm did not exceed 6 minutes, and we achieved an average sensitivity of 82.1% and an average precision of 13.3%.

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“…Both [9] and [5] report a very high number of false alarms and also consider a lymph node already as detected if there is just overlap with the automatic segmentation. In [3], very good results are reported, but the method was evaluated on a single dataset. In [1], good results are reported for the axillary region.…”

Section: Resultsmentioning

confidence: 99%

“…In [9,5], lymph nodes are detected by a cascade of filters (Hessian based, morphological operations and a so-called 3-D Min-DD filter). In [3], lymph nodes are detected and segmented by fitting a mass-spring model at different positions. In this paper, we follow [6,1] that proposed two data driven approaches.…”

Section: Introductionmentioning

confidence: 99%

Segmentation Based Features for Lymph Node Detection from 3-D Chest CT

Feulner

Zhou

Hammon

et al. 2011

Machine Learning in Medical Imaging

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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.

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Automatische Detektion von Lymphknoten in CT-Datensätzen des Halses

Cited by 11 publications

References 6 publications

Automatic Detection and Segmentation of Axillary Lymph Nodes

Automatic Detection and Segmentation of Axillary Lymph Nodes

Automatic mediastinal lymph node detection in chest CT

Segmentation Based Features for Lymph Node Detection from 3-D Chest CT

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