Automatic detection of mediastinal lymph nodes using 3D convolutional neural network

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dc.contributor.authorPaing, May Phu
dc.contributor.authorPintavirooj, Chuchart
dc.contributor.authorTungjitkusolmun, Supan
dc.contributor.authorWin, Kyi Pyar
dc.contributor.authorHamamoto, Kazuhiko
dc.date.accessioned2026-08-06T10:25:46Z
dc.date.available2026-08-06T10:25:46Z
dc.date.issued2019-09-16
dc.description.abstractMediastinal lymph nodes are one of the most critical factors to identify the clinical stages of lung cancer. As the lymph nodes are low in attenuation and cluttering with various shapes and sizes, manual detection is usually error-prone and effort-intensive. This paper introduces a method for automatic detection of mediastinal lymph nodes by proposing three significant contributions. First, we constraint the detection area, mediastinal region, using greylevel thresholding. Next, we apply the watershed method and hessian eigenvalues to separate a cluster of lymph nodes. Finally, we build a three-dimensional convolutional neural network (3D CNN) to distinguish the actual lymph nodes from other false lesions. Our experiment is conducted using 70 CT exams containing 314 lymph nodes and achieved a favorable result with 94 % detection rate.
dc.identifier.citationACM International Conference Proceeding Series, 26-31, 2019
dc.identifier.doi10.1145/3366174.3366182
dc.identifier.other2-s2.0-85078330934
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10201
dc.sourceACM International Conference Proceeding Series
dc.subjectComputed tomography
dc.subjectConvolutional neural network
dc.subjectLung cancer
dc.subjectLymph nodes
dc.titleAutomatic detection of mediastinal lymph nodes using 3D convolutional neural network
dc.typeConference Paper

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