Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN

Kanta Miura; Koichi Ito; Takafumi Aoki; Jun Ohmiya; Satoshi Kondo

doi:10.1007/978-3-030-87583-1_10

Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN

Kanta Miura, Koichi Ito, Takafumi Aoki, Jun Ohmiya, Satoshi Kondo

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

Abstract

In this paper, we propose an ultrasound (US) probe pose estimation method only from US image sequences using deep learning for volume reconstruction. The proposed method employs the combination of convolutional neural network (CNN) and recurrent neural network (RNN) to estimate the US probe pose in light of the long-term temporal information of US image sequences. The features extracted by CNN are input to RNN to estimate the relative and absolute pose of the US probe. Through a set of experiments using US image sequence datasets with ground-truth pose measured by an optical tracking system, we demonstrate that the proposed method exhibits the efficient performance on US probe pose estimation and volume reconstruction compared with the conventional method.

Original language	English
Title of host publication	Simplifying Medical Ultrasound - Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	J. Alison Noble, Stephen Aylward, Alexander Grimwood, Zhe Min, Su-Lin Lee, Yipeng Hu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	96-105
Number of pages	10
ISBN (Print)	9783030875824
DOIs	https://doi.org/10.1007/978-3-030-87583-1_10
Publication status	Published - 2021
Event	2nd International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 - Strasbourg, France Duration: 2021 Sept 27 → 2021 Sept 27

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12967 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021
Country/Territory	France
City	Strasbourg
Period	21/9/27 → 21/9/27

Keywords

CNN
Probe pose estimation
RNN
Ultrasound
Volume reconstruction

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-87583-1_10

Cite this

Miura, K., Ito, K., Aoki, T., Ohmiya, J., & Kondo, S. (2021). Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN. In J. A. Noble, S. Aylward, A. Grimwood, Z. Min, S-L. Lee, & Y. Hu (Eds.), Simplifying Medical Ultrasound - Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 96-105). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12967 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87583-1_10

Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN. / Miura, Kanta; Ito, Koichi ; Aoki, Takafumi et al.

Simplifying Medical Ultrasound - Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / J. Alison Noble; Stephen Aylward; Alexander Grimwood; Zhe Min; Su-Lin Lee; Yipeng Hu. Springer Science and Business Media Deutschland GmbH, 2021. p. 96-105 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12967 LNCS).

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

Miura, K, Ito, K , Aoki, T, Ohmiya, J & Kondo, S 2021, Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN. in JA Noble, S Aylward, A Grimwood, Z Min, S-L Lee & Y Hu (eds), Simplifying Medical Ultrasound - Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12967 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 96-105, 2nd International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, Strasbourg, France, 21/9/27. https://doi.org/10.1007/978-3-030-87583-1_10

Miura K, Ito K , Aoki T, Ohmiya J, Kondo S. Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN. In Noble JA, Aylward S, Grimwood A, Min Z, Lee S-L, Hu Y, editors, Simplifying Medical Ultrasound - Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 96-105. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-87583-1_10

Miura, Kanta ; Ito, Koichi ; Aoki, Takafumi et al. / Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN. Simplifying Medical Ultrasound - Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / J. Alison Noble ; Stephen Aylward ; Alexander Grimwood ; Zhe Min ; Su-Lin Lee ; Yipeng Hu. Springer Science and Business Media Deutschland GmbH, 2021. pp. 96-105 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we propose an ultrasound (US) probe pose estimation method only from US image sequences using deep learning for volume reconstruction. The proposed method employs the combination of convolutional neural network (CNN) and recurrent neural network (RNN) to estimate the US probe pose in light of the long-term temporal information of US image sequences. The features extracted by CNN are input to RNN to estimate the relative and absolute pose of the US probe. Through a set of experiments using US image sequence datasets with ground-truth pose measured by an optical tracking system, we demonstrate that the proposed method exhibits the efficient performance on US probe pose estimation and volume reconstruction compared with the conventional method.",

keywords = "CNN, Probe pose estimation, RNN, Ultrasound, Volume reconstruction",

author = "Kanta Miura and Koichi Ito and Takafumi Aoki and Jun Ohmiya and Satoshi Kondo",

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AB - In this paper, we propose an ultrasound (US) probe pose estimation method only from US image sequences using deep learning for volume reconstruction. The proposed method employs the combination of convolutional neural network (CNN) and recurrent neural network (RNN) to estimate the US probe pose in light of the long-term temporal information of US image sequences. The features extracted by CNN are input to RNN to estimate the relative and absolute pose of the US probe. Through a set of experiments using US image sequence datasets with ground-truth pose measured by an optical tracking system, we demonstrate that the proposed method exhibits the efficient performance on US probe pose estimation and volume reconstruction compared with the conventional method.

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Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN

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