Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity

Pan Su, Yitian Zhao, Tianhua Chen, Jianyang Xie, Yifan Zhao, Hong Qi, Yalin Zheng, Jiang Liu

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

Abstract

The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention - MICCAI 2019
Subtitle of host publication22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Place of PublicationCham
PublisherSpringer, Cham
Pages12-20
Number of pages9
VolumeLNCS11767
Edition1st
ISBN (Electronic)9783030322519
ISBN (Print)9783030322502
DOIs
Publication statusE-pub ahead of print - 10 Oct 2019
Event22nd International Conference on Medical Image Computing and Computer Assisted Intervention - Hotel Shenzhen, Shenzhen, China
Duration: 13 Oct 201917 Oct 2019
Conference number: 22
https://www.miccai2019.org/

Publication series

NameLecture Notes in Computer Science
VolumeLNCS11767

Conference

Conference22nd International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2019
CountryChina
CityShenzhen
Period13/10/1917/10/19
Internet address

Fingerprint

Agglomeration
Fuzzy clustering
Pipelines

Cite this

Su, P., Zhao, Y., Chen, T., Xie, J., Zhao, Y., Qi, H., ... Liu, J. (2019). Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity. In D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P-T. Yap, ... A. Khan (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV (1st ed., Vol. LNCS11767, pp. 12-20). (Lecture Notes in Computer Science; Vol. LNCS11767). Cham: Springer, Cham. https://doi.org/10.1007%2F978-3-030-32251-9_2
Su, Pan ; Zhao, Yitian ; Chen, Tianhua ; Xie, Jianyang ; Zhao, Yifan ; Qi, Hong ; Zheng, Yalin ; Liu, Jiang. / Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity. Medical Image Computing and Computer Assisted Intervention - MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV . editor / Dinggang Shen ; Tianming Liu ; Terry M. Peters ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou ; Pew-Thian Yap ; Ali Khan. Vol. LNCS11767 1st. ed. Cham : Springer, Cham, 2019. pp. 12-20 (Lecture Notes in Computer Science).
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abstract = "The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.",
keywords = "Tortuosity assessment, Curvilinear structure, Fuzzy clustering, Reliability guided aggregation",
author = "Pan Su and Yitian Zhao and Tianhua Chen and Jianyang Xie and Yifan Zhao and Hong Qi and Yalin Zheng and Jiang Liu",
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Su, P, Zhao, Y, Chen, T, Xie, J, Zhao, Y, Qi, H, Zheng, Y & Liu, J 2019, Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity. in D Shen, T Liu, TM Peters, LH Staib, C Essert, S Zhou, P-T Yap & A Khan (eds), Medical Image Computing and Computer Assisted Intervention - MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV . 1st edn, vol. LNCS11767, Lecture Notes in Computer Science, vol. LNCS11767, Springer, Cham, Cham, pp. 12-20, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, China, 13/10/19. https://doi.org/10.1007%2F978-3-030-32251-9_2

Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity. / Su, Pan ; Zhao, Yitian; Chen, Tianhua; Xie, Jianyang; Zhao, Yifan; Qi, Hong; Zheng, Yalin; Liu, Jiang.

Medical Image Computing and Computer Assisted Intervention - MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV . ed. / Dinggang Shen; Tianming Liu; Terry M. Peters; Lawrence H. Staib; Caroline Essert; Sean Zhou; Pew-Thian Yap; Ali Khan. Vol. LNCS11767 1st. ed. Cham : Springer, Cham, 2019. p. 12-20 (Lecture Notes in Computer Science; Vol. LNCS11767).

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

TY - GEN

T1 - Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity

AU - Su, Pan

AU - Zhao, Yitian

AU - Chen, Tianhua

AU - Xie, Jianyang

AU - Zhao, Yifan

AU - Qi, Hong

AU - Zheng, Yalin

AU - Liu, Jiang

PY - 2019/10/10

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N2 - The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.

AB - The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.

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KW - Reliability guided aggregation

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M3 - Conference contribution

SN - 9783030322502

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T3 - Lecture Notes in Computer Science

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BT - Medical Image Computing and Computer Assisted Intervention - MICCAI 2019

A2 - Shen, Dinggang

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

A2 - Yap, Pew-Thian

A2 - Khan, Ali

PB - Springer, Cham

CY - Cham

ER -

Su P, Zhao Y, Chen T, Xie J, Zhao Y, Qi H et al. Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity. In Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P-T, Khan A, editors, Medical Image Computing and Computer Assisted Intervention - MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV . 1st ed. Vol. LNCS11767. Cham: Springer, Cham. 2019. p. 12-20. (Lecture Notes in Computer Science). https://doi.org/10.1007%2F978-3-030-32251-9_2