Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients

Zhen Yuan; Esther Puyol-Antón; Haran Jogeesvaran; Catriona Reid; Baba Inusa; Andrew P. King

doi:10.1007/978-3-030-60334-2_4

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients

Zhen Yuan^*, Esther Puyol-Antón, Haran Jogeesvaran, Catriona Reid, Baba Inusa, Andrew P. King

^*Corresponding author for this work

Biomedical Engineering Department

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

1 Citation (Scopus)

Abstract

Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Splenomegaly (abnormal enlargement of the spleen) is frequent among children with SCD. If left untreated, splenomegaly can be life-threatening. The current workflow to measure spleen size includes palpation, possibly followed by manual length measurement in 2D ultrasound imaging. However, this manual measurement is dependent on operator expertise and is subject to intra- and inter-observer variability. We investigate the use of deep learning to perform automatic estimation of spleen length from ultrasound images. We investigate two types of approach, one segmentation-based and one based on direct length estimation, and compare the results against measurements made by human experts. Our best model (segmentation-based) achieved a percentage length error of 7.42%, which is approaching the level of inter-observer variability (5.47%–6.34%). To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.

Original language	English
Title of host publication	Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editors	Yipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	33-41
Number of pages	9
ISBN (Print)	9783030603335
DOIs	https://doi.org/10.1007/978-3-030-60334-2_4
Publication status	Published - 2020
Event	1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12437 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	4/10/2020 → 8/10/2020

Keywords

Deep learning
Sickle Cell Disease
Spleen ultrasound images

UN SDGs

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

Access to Document

10.1007/978-3-030-60334-2_4

Cite this

Yuan, Z., Puyol-Antón, E., Jogeesvaran, H., Reid, C., Inusa, B., & King, A. P. (2020). Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients. In Y. Hu, R. Licandro, J. A. Noble, J. Hutter, A. Melbourne, S. Aylward, E. Abaci Turk, J. Torrents Barrena, & J. Torrents Barrena (Eds.), Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings (pp. 33-41). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12437 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60334-2_4

Yuan, Zhen ; Puyol-Antón, Esther ; Jogeesvaran, Haran et al. / Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients. Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings. editor / Yipeng Hu ; Roxane Licandro ; J. Alison Noble ; Jana Hutter ; Andrew Melbourne ; Stephen Aylward ; Esra Abaci Turk ; Jordina Torrents Barrena ; Jordina Torrents Barrena. Springer Science and Business Media Deutschland GmbH, 2020. pp. 33-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients",

abstract = "Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Splenomegaly (abnormal enlargement of the spleen) is frequent among children with SCD. If left untreated, splenomegaly can be life-threatening. The current workflow to measure spleen size includes palpation, possibly followed by manual length measurement in 2D ultrasound imaging. However, this manual measurement is dependent on operator expertise and is subject to intra- and inter-observer variability. We investigate the use of deep learning to perform automatic estimation of spleen length from ultrasound images. We investigate two types of approach, one segmentation-based and one based on direct length estimation, and compare the results against measurements made by human experts. Our best model (segmentation-based) achieved a percentage length error of 7.42%, which is approaching the level of inter-observer variability (5.47%–6.34%). To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.",

keywords = "Deep learning, Sickle Cell Disease, Spleen ultrasound images",

author = "Zhen Yuan and Esther Puyol-Ant{\'o}n and Haran Jogeesvaran and Catriona Reid and Baba Inusa and King, {Andrew P.}",

year = "2020",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Yipeng Hu and Roxane Licandro and Noble, {J. Alison} and Jana Hutter and Andrew Melbourne and Stephen Aylward and {Abaci Turk}, Esra and {Torrents Barrena}, Jordina and {Torrents Barrena}, Jordina",

booktitle = "Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings",

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note = "1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

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Yuan, Z, Puyol-Antón, E, Jogeesvaran, H, Reid, C, Inusa, B & King, AP 2020, Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients. in Y Hu, R Licandro, JA Noble, J Hutter, A Melbourne, S Aylward, E Abaci Turk, J Torrents Barrena & J Torrents Barrena (eds), Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12437 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 33-41, 1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 4/10/2020. https://doi.org/10.1007/978-3-030-60334-2_4

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients. / Yuan, Zhen; Puyol-Antón, Esther; Jogeesvaran, Haran et al.
Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings. ed. / Yipeng Hu; Roxane Licandro; J. Alison Noble; Jana Hutter; Andrew Melbourne; Stephen Aylward; Esra Abaci Turk; Jordina Torrents Barrena; Jordina Torrents Barrena. Springer Science and Business Media Deutschland GmbH, 2020. p. 33-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12437 LNCS).

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

TY - CHAP

T1 - Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients

AU - Yuan, Zhen

AU - Puyol-Antón, Esther

AU - Jogeesvaran, Haran

AU - Reid, Catriona

AU - Inusa, Baba

AU - King, Andrew P.

PY - 2020

Y1 - 2020

N2 - Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Splenomegaly (abnormal enlargement of the spleen) is frequent among children with SCD. If left untreated, splenomegaly can be life-threatening. The current workflow to measure spleen size includes palpation, possibly followed by manual length measurement in 2D ultrasound imaging. However, this manual measurement is dependent on operator expertise and is subject to intra- and inter-observer variability. We investigate the use of deep learning to perform automatic estimation of spleen length from ultrasound images. We investigate two types of approach, one segmentation-based and one based on direct length estimation, and compare the results against measurements made by human experts. Our best model (segmentation-based) achieved a percentage length error of 7.42%, which is approaching the level of inter-observer variability (5.47%–6.34%). To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.

AB - Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Splenomegaly (abnormal enlargement of the spleen) is frequent among children with SCD. If left untreated, splenomegaly can be life-threatening. The current workflow to measure spleen size includes palpation, possibly followed by manual length measurement in 2D ultrasound imaging. However, this manual measurement is dependent on operator expertise and is subject to intra- and inter-observer variability. We investigate the use of deep learning to perform automatic estimation of spleen length from ultrasound images. We investigate two types of approach, one segmentation-based and one based on direct length estimation, and compare the results against measurements made by human experts. Our best model (segmentation-based) achieved a percentage length error of 7.42%, which is approaching the level of inter-observer variability (5.47%–6.34%). To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.

KW - Deep learning

KW - Sickle Cell Disease

KW - Spleen ultrasound images

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U2 - 10.1007/978-3-030-60334-2_4

DO - 10.1007/978-3-030-60334-2_4

M3 - Conference paper

AN - SCOPUS:85092747305

SN - 9783030603335

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

SP - 33

EP - 41

BT - Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings

A2 - Hu, Yipeng

A2 - Licandro, Roxane

A2 - Noble, J. Alison

A2 - Hutter, Jana

A2 - Melbourne, Andrew

A2 - Aylward, Stephen

A2 - Abaci Turk, Esra

A2 - Torrents Barrena, Jordina

PB - Springer Science and Business Media Deutschland GmbH

T2 - 1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

Y2 - 4 October 2020 through 8 October 2020

ER -

Yuan Z, Puyol-Antón E, Jogeesvaran H, Reid C, Inusa B , King AP. Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients. In Hu Y, Licandro R, Noble JA, Hutter J, Melbourne A, Aylward S, Abaci Turk E, Torrents Barrena J, Torrents Barrena J, editors, Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 33-41. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60334-2_4

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients

Abstract

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Keywords

UN SDGs

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