Novel method and applications for labeling and identifying lymph nodes

Kiraly, Atilla P.; Naidich, David P.; Guendel, Lutz; Zhang, Li; Novak, Carol L.

doi:10.1117/12.709413

Cited by 7 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[3][4][5][6] Seed points required to start the segmentation process could be automatically selected from the output of our method and, once the segmentation is automated, assignment methods for regional lymph node stations can be further automated. 7,8 Eventually this will establish a more sophisticated computer aided diagnosis of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

“…While approaches for the segmentation of lymph nodes in CT images based on a few mouse clicks or more interaction [3][4][5][6] and for the automatic assignment of regional lymph node stations 7,8 have been proposed previously, little work has been done on the automatic detection of lymph nodes. To our knowledge, so far only three approaches [9][10][11] to (semi-)automatic lymph node detection in CT datasets were presented.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic mediastinal lymph node detection in chest CT

Feuerstein

Deguchi

Kitasaka

et al. 2009

Medical Imaging 2009: Computer-Aided Diagnosis

View full text Add to dashboard Cite

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%.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic mediastinal lymph node detection in chest CT

Feuerstein

Deguchi

Kitasaka

et al. 2009

Medical Imaging 2009: Computer-Aided Diagnosis

View full text Add to dashboard Cite

show abstract

“…Other segmentation methods such as [3,6,11] could possibly be used, but they lack a robust data term, making them more prone to oversegmentations in low gradient locations. …”

Section: I(c) − I(cmentioning

confidence: 99%

Automatic Detection and Segmentation of Axillary Lymph Nodes

Barbu

Suehling

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

View full text Add to dashboard Cite

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.

show abstract

“…1, before the actual labeling process begins, anatomical key structures such as airways, aorta, pulmonary artery, and sternum need to be detected to define the nodal stations [25,26], which itself can be time-consuming and error-prone. Using the airways and the aorta as anatomical landmarks and machine learning to train the labeling process, the best achievable station labeling accuracy to date was 76% [25].…”

Section: Introductionmentioning

confidence: 99%

“…The publications can be mainly categorized into detection [12,13,14,15,16] and segmentation [17,18,19,20,21,22,23,24] of lymph nodes, labeling of lymph node stations [25,26], and computer assisted treatment planning [27]. For the segmentation of lymph nodes, recently also a commercial system has been announced [28].…”

Section: Introductionmentioning

confidence: 99%

Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes

Feuerstein

Glocker

Kitasaka

et al. 2012

Medical Image Analysis

View full text Add to dashboard Cite

One important aspect of lung cancer staging is the assessment of mediastinal lymph nodes in 3-D chest computed tomography (CT) images. In the current clinical routine this is done manually by analyzing the 3-D CT image slice by slice to find nodes, evaluate them quantitatively, and assign labels to them for describing the clinical and pathologic extent of metastases. In this paper we present a method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastinal, aortic, and N1 nodes. Utilizing a fast deformable registration approach to match the atlas with CT images of new patients, our method can maintain an acceptable runtime. In comparison to previously published methods for mediastinal lymph node detection and labeling it also shows a good sensitivity and positive predictive value.

show abstract

Novel method and applications for labeling and identifying lymph nodes

Cited by 7 publications

References 0 publications

Automatic mediastinal lymph node detection in chest CT

Automatic mediastinal lymph node detection in chest CT

Automatic Detection and Segmentation of Axillary Lymph Nodes

Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes

Contact Info

Product

Resources

About