Classification of suspected liver metastases using fMRI images: A machine learning approach

Moti Freiman; Yifat Edrei; Yehonatan Sela; Yitzchak Shmidmayer; Eitan Gross; Leo Joskowicz; Rinat Abramovitch

doi:10.1007/978-3-540-85988-8_12

Classification of suspected liver metastases using fMRI images: A machine learning approach

Moti Freiman^*
, Yifat Edrei
, Yehonatan Sela
, Yitzchak Shmidmayer
, Eitan Gross
, Leo Joskowicz
, Rinat Abramovitch

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

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

6 Scopus citations

Abstract

This paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fMRI images. The method uses fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from -W fMRI images acquired during the breathing of air, air-CO₂, and carbogen. A classification model is build to differentiate between tumors and healthy liver tissues. To validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. Our experimental results on mice yielded an accuracy of 78% with high precision (88%). This suggests that the method can provide a useful aid for early detection of liver metastases.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
Pages	93-100
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-540-85988-8_12
State	Published - 2008
Event	11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, United States Duration: 6 Sep 2008 → 10 Sep 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5241 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
Country/Territory	United States
City	New York, NY
Period	6/09/08 → 10/09/08

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-540-85988-8_12

Cite this

Freiman, M., Edrei, Y., Sela, Y., Shmidmayer, Y., Gross, E., Joskowicz, L., & Abramovitch, R. (2008). Classification of suspected liver metastases using fMRI images: A machine learning approach. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings (PART 1 ed., pp. 93-100). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-85988-8_12

Freiman, Moti ; Edrei, Yifat ; Sela, Yehonatan et al. / Classification of suspected liver metastases using fMRI images : A machine learning approach. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. pp. 93-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{ad68245891f84424bd1ee55d8a74356d,

title = "Classification of suspected liver metastases using fMRI images: A machine learning approach",

abstract = "This paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fMRI images. The method uses fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from -W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model is build to differentiate between tumors and healthy liver tissues. To validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. Our experimental results on mice yielded an accuracy of 78\% with high precision (88\%). This suggests that the method can provide a useful aid for early detection of liver metastases.",

author = "Moti Freiman and Yifat Edrei and Yehonatan Sela and Yitzchak Shmidmayer and Eitan Gross and Leo Joskowicz and Rinat Abramovitch",

year = "2008",

doi = "10.1007/978-3-540-85988-8\_12",

language = "אנגלית",

isbn = "354085987X",

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

number = "PART 1",

pages = "93--100",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings",

edition = "PART 1",

note = "11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 ; Conference date: 06-09-2008 Through 10-09-2008",

}

Freiman, M, Edrei, Y, Sela, Y, Shmidmayer, Y, Gross, E, Joskowicz, L & Abramovitch, R 2008, Classification of suspected liver metastases using fMRI images: A machine learning approach. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5241 LNCS, pp. 93-100, 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, New York, NY, United States, 6/09/08. https://doi.org/10.1007/978-3-540-85988-8_12

Classification of suspected liver metastases using fMRI images: A machine learning approach. / Freiman, Moti; Edrei, Yifat; Sela, Yehonatan et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. p. 93-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1).

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

TY - GEN

T1 - Classification of suspected liver metastases using fMRI images

T2 - 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008

AU - Freiman, Moti

AU - Edrei, Yifat

AU - Sela, Yehonatan

AU - Shmidmayer, Yitzchak

AU - Gross, Eitan

AU - Joskowicz, Leo

AU - Abramovitch, Rinat

PY - 2008

Y1 - 2008

N2 - This paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fMRI images. The method uses fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from -W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model is build to differentiate between tumors and healthy liver tissues. To validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. Our experimental results on mice yielded an accuracy of 78% with high precision (88%). This suggests that the method can provide a useful aid for early detection of liver metastases.

AB - This paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fMRI images. The method uses fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from -W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model is build to differentiate between tumors and healthy liver tissues. To validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. Our experimental results on mice yielded an accuracy of 78% with high precision (88%). This suggests that the method can provide a useful aid for early detection of liver metastases.

UR - https://www.scopus.com/pages/publications/79952404009

U2 - 10.1007/978-3-540-85988-8_12

DO - 10.1007/978-3-540-85988-8_12

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

C2 - 18979736

AN - SCOPUS:79952404009

SN - 354085987X

SN - 9783540859871

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

SP - 93

EP - 100

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings

Y2 - 6 September 2008 through 10 September 2008

ER -

Freiman M, Edrei Y, Sela Y, Shmidmayer Y, Gross E, Joskowicz L et al. Classification of suspected liver metastases using fMRI images: A machine learning approach. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 ed. 2008. p. 93-100. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-85988-8_12

Classification of suspected liver metastases using fMRI images: A machine learning approach

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this