An automatic visual inspection method based on supervised machine learning for rapid on-site evaluation in EUS-FNA

Hirofumi Inoue, Kazuki Ogo, Motohiro Tabuchi, Nobumoto Yamane, Hisao Oka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, an automatic visual inspection method based on supervised machine learning is proposed to assist rapid on-site evaluation (ROSE) for endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) biopsy. The aim of this method is to learn relations between content of cellular tissue including tumor cells and aspect of specimen image removed by the needle aspiration. For this purpose, a stationary Gaussian mixture model (GMM) is applied to classify the local statistics of the specimen images, because stationary GMM is known to be effective to estimate universal model. In this paper, some specimen images with their definitive diagnosis information are used as training images in GMM learning. The training images are also used in the supervised learning with their diagnosis information as teacher data, i.e. the rank of tumor cells content. Thus, the learning of statistical relation between the local image aspect and its rank of tumor cells content may be linked by the class index of GMM, using the training images. A simulation result shows that the proposed method is effective to assist on-site visual inspection of cellular tissue in ROSE for EUS-FNA, indicating highly probable area including tumor cells.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
PublisherSociety of Instrument and Control Engineers (SICE)
Pages1114-1119
Number of pages6
ISBN (Print)9784907764463
DOIs
Publication statusPublished - Oct 23 2014
Event2014 53rd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2014 - Sapporo, Japan
Duration: Sep 9 2014Sep 12 2014

Other

Other2014 53rd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2014
CountryJapan
CitySapporo
Period9/9/149/12/14

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Keywords

  • automatic visual inspection
  • EUS-FNA
  • Gaussian mixture model
  • rapid on-site evaluation
  • supervised machine leaning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

Cite this

Inoue, H., Ogo, K., Tabuchi, M., Yamane, N., & Oka, H. (2014). An automatic visual inspection method based on supervised machine learning for rapid on-site evaluation in EUS-FNA. In Proceedings of the SICE Annual Conference (pp. 1114-1119). [6935253] Society of Instrument and Control Engineers (SICE). https://doi.org/10.1109/SICE.2014.6935253