Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning

Pranjal Sahu; Hailiang Huang; Wei Zhao; Hong Qin

doi:10.1007/978-3-030-87234-2_6

Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning

Pranjal Sahu
, Hailiang Huang
, Wei Zhao
, Hong Qin

Stony Brook University

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

2 Scopus citations

Abstract

Medical image reconstruction algorithms such as Penalized Weighted Least Squares (PWLS) typically rely on a good choice of tuning parameters such as the number of iterations, the strength of regularizar, etc. However, obtaining a good estimate of such parameters is often done using trial and error methods. This process is very time consuming and laborious especially for high resolution images. To solve this problem we propose an interactive framework. We focus on the regularization parameter and train a CNN to imitate its impact on image for varying values. The trained CNN can be used by a human practitioner to tune the regularization strength on-the-fly as per the requirements. Taking the example of Digital Breast Tomosynthesis reconstruction, we demonstrate the feasibility of our approach and also discuss the future applications of this interactive reconstruction approach. We also test the proposed methodology on public Walnut and Lodopab CT reconstruction datasets to show it can be generalized to CT reconstruction as well.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
Editors	Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	57-67
Number of pages	11
ISBN (Print)	9783030872335
DOIs	https://doi.org/10.1007/978-3-030-87234-2_6
State	Published - 2021
Event	24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: Sep 27 2021 → Oct 1 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12907 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	09/27/21 → 10/1/21

Keywords

Breast Tomosynthesis reconstruction
CNN
Deep Learning

Access to Document

10.1007/978-3-030-87234-2_6

Cite this

Sahu, P., Huang, H., Zhao, W., & Qin, H. (2021). Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning. In M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings (pp. 57-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12907 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87234-2_6

Sahu, Pranjal ; Huang, Hailiang ; Zhao, Wei et al. / Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. editor / Marleen de Bruijne ; Philippe C. Cattin ; Stéphane Cotin ; Nicolas Padoy ; Stefanie Speidel ; Yefeng Zheng ; Caroline Essert. Springer Science and Business Media Deutschland GmbH, 2021. pp. 57-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{601e16e2d04048caa6399a5e74bfa1a3,

title = "Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning",

abstract = "Medical image reconstruction algorithms such as Penalized Weighted Least Squares (PWLS) typically rely on a good choice of tuning parameters such as the number of iterations, the strength of regularizar, etc. However, obtaining a good estimate of such parameters is often done using trial and error methods. This process is very time consuming and laborious especially for high resolution images. To solve this problem we propose an interactive framework. We focus on the regularization parameter and train a CNN to imitate its impact on image for varying values. The trained CNN can be used by a human practitioner to tune the regularization strength on-the-fly as per the requirements. Taking the example of Digital Breast Tomosynthesis reconstruction, we demonstrate the feasibility of our approach and also discuss the future applications of this interactive reconstruction approach. We also test the proposed methodology on public Walnut and Lodopab CT reconstruction datasets to show it can be generalized to CT reconstruction as well.",

keywords = "Breast Tomosynthesis reconstruction, CNN, Deep Learning",

author = "Pranjal Sahu and Hailiang Huang and Wei Zhao and Hong Qin",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

year = "2021",

doi = "10.1007/978-3-030-87234-2\_6",

language = "English",

isbn = "9783030872335",

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

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booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings",

}

Sahu, P, Huang, H, Zhao, W & Qin, H 2021, Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning. in M de Bruijne, PC Cattin, S Cotin, N Padoy, S Speidel, Y Zheng & C Essert (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12907 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 57-67, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 09/27/21. https://doi.org/10.1007/978-3-030-87234-2_6

Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning. / Sahu, Pranjal; Huang, Hailiang; Zhao, Wei et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. ed. / Marleen de Bruijne; Philippe C. Cattin; Stéphane Cotin; Nicolas Padoy; Stefanie Speidel; Yefeng Zheng; Caroline Essert. Springer Science and Business Media Deutschland GmbH, 2021. p. 57-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12907 LNCS).

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

TY - GEN

T1 - Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning

AU - Sahu, Pranjal

AU - Huang, Hailiang

AU - Zhao, Wei

AU - Qin, Hong

PY - 2021

Y1 - 2021

N2 - Medical image reconstruction algorithms such as Penalized Weighted Least Squares (PWLS) typically rely on a good choice of tuning parameters such as the number of iterations, the strength of regularizar, etc. However, obtaining a good estimate of such parameters is often done using trial and error methods. This process is very time consuming and laborious especially for high resolution images. To solve this problem we propose an interactive framework. We focus on the regularization parameter and train a CNN to imitate its impact on image for varying values. The trained CNN can be used by a human practitioner to tune the regularization strength on-the-fly as per the requirements. Taking the example of Digital Breast Tomosynthesis reconstruction, we demonstrate the feasibility of our approach and also discuss the future applications of this interactive reconstruction approach. We also test the proposed methodology on public Walnut and Lodopab CT reconstruction datasets to show it can be generalized to CT reconstruction as well.

AB - Medical image reconstruction algorithms such as Penalized Weighted Least Squares (PWLS) typically rely on a good choice of tuning parameters such as the number of iterations, the strength of regularizar, etc. However, obtaining a good estimate of such parameters is often done using trial and error methods. This process is very time consuming and laborious especially for high resolution images. To solve this problem we propose an interactive framework. We focus on the regularization parameter and train a CNN to imitate its impact on image for varying values. The trained CNN can be used by a human practitioner to tune the regularization strength on-the-fly as per the requirements. Taking the example of Digital Breast Tomosynthesis reconstruction, we demonstrate the feasibility of our approach and also discuss the future applications of this interactive reconstruction approach. We also test the proposed methodology on public Walnut and Lodopab CT reconstruction datasets to show it can be generalized to CT reconstruction as well.

KW - Breast Tomosynthesis reconstruction

KW - CNN

KW - Deep Learning

UR - https://www.scopus.com/pages/publications/85116507892

U2 - 10.1007/978-3-030-87234-2_6

DO - 10.1007/978-3-030-87234-2_6

M3 - Conference contribution

AN - SCOPUS:85116507892

SN - 9783030872335

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

SP - 57

EP - 67

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings

A2 - de Bruijne, Marleen

A2 - Cattin, Philippe C.

A2 - Cotin, Stéphane

A2 - Padoy, Nicolas

A2 - Speidel, Stefanie

A2 - Zheng, Yefeng

A2 - Essert, Caroline

PB - Springer Science and Business Media Deutschland GmbH

T2 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021

Y2 - 27 September 2021 through 1 October 2021

ER -

Sahu P, Huang H, Zhao W , Qin H. Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning. In de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, Essert C, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 57-67. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-87234-2_6

Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning

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Keywords

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