Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble

Konstantin Dmitriev; Arie E. Kaufman; Ammar A. Javed; Ralph H. Hruban; Elliot K. Fishman; Anne Marie Lennon; Joel H. Saltz

doi:10.1007/978-3-319-66179-7_18

Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble

Konstantin Dmitriev
, Arie E. Kaufman
, Ammar A. Javed
, Ralph H. Hruban
, Elliot K. Fishman
, Anne Marie Lennon
, Joel H. Saltz

Stony Brook University
Johns Hopkins University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

67 Scopus citations

Abstract

There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6%.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
Editors	Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins
Publisher	Springer Verlag
Pages	150-158
Number of pages	9
ISBN (Print)	9783319661780
DOIs	https://doi.org/10.1007/978-3-319-66179-7_18
State	Published - 2017
Event	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: Sep 11 2017 → Sep 13 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10435 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	09/11/17 → 09/13/17

Access to Document

10.1007/978-3-319-66179-7_18

Cite this

Dmitriev, K., Kaufman, A. E., Javed, A. A., Hruban, R. H., Fishman, E. K., Lennon, A. M., & Saltz, J. H. (2017). Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble. In L. Maier-Hein, A. Franz, P. Jannin, S. Duchesne, M. Descoteaux, & D. L. Collins (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (pp. 150-158). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10435 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66179-7_18

Dmitriev, Konstantin ; Kaufman, Arie E. ; Javed, Ammar A. et al. / Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. editor / Lena Maier-Hein ; Alfred Franz ; Pierre Jannin ; Simon Duchesne ; Maxime Descoteaux ; D. Louis Collins. Springer Verlag, 2017. pp. 150-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5b64d2d32b094e63b49dcfe052ca4dc0,

title = "Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble",

abstract = "There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6\%.",

author = "Konstantin Dmitriev and Kaufman, \{Arie E.\} and Javed, \{Ammar A.\} and Hruban, \{Ralph H.\} and Fishman, \{Elliot K.\} and Lennon, \{Anne Marie\} and Saltz, \{Joel H.\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 ; Conference date: 11-09-2017 Through 13-09-2017",

year = "2017",

doi = "10.1007/978-3-319-66179-7\_18",

language = "English",

isbn = "9783319661780",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "150--158",

editor = "Lena Maier-Hein and Alfred Franz and Pierre Jannin and Simon Duchesne and Maxime Descoteaux and Collins, \{D. Louis\}",

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}

Dmitriev, K, Kaufman, AE, Javed, AA, Hruban, RH, Fishman, EK, Lennon, AM & Saltz, JH 2017, Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble. in L Maier-Hein, A Franz, P Jannin, S Duchesne, M Descoteaux & DL Collins (eds), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10435 LNCS, Springer Verlag, pp. 150-158, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 09/11/17. https://doi.org/10.1007/978-3-319-66179-7_18

Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble. / Dmitriev, Konstantin; Kaufman, Arie E.; Javed, Ammar A. et al.
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. ed. / Lena Maier-Hein; Alfred Franz; Pierre Jannin; Simon Duchesne; Maxime Descoteaux; D. Louis Collins. Springer Verlag, 2017. p. 150-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10435 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble

AU - Dmitriev, Konstantin

AU - Kaufman, Arie E.

AU - Javed, Ammar A.

AU - Hruban, Ralph H.

AU - Fishman, Elliot K.

AU - Lennon, Anne Marie

AU - Saltz, Joel H.

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6%.

AB - There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6%.

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DO - 10.1007/978-3-319-66179-7_18

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SN - 9783319661780

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 150

EP - 158

BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings

A2 - Maier-Hein, Lena

A2 - Franz, Alfred

A2 - Jannin, Pierre

A2 - Duchesne, Simon

A2 - Descoteaux, Maxime

A2 - Collins, D. Louis

PB - Springer Verlag

T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017

Y2 - 11 September 2017 through 13 September 2017

ER -

Dmitriev K, Kaufman AE, Javed AA, Hruban RH, Fishman EK, Lennon AM et al. Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble. In Maier-Hein L, Franz A, Jannin P, Duchesne S, Descoteaux M, Collins DL, editors, Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Springer Verlag. 2017. p. 150-158. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66179-7_18

Classification of pancreatic cysts in computed tomography images using a random forest and convolutional neural network ensemble

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