Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage

Vidya Prasad; Yogish Mallya; Arun Shastry; Vijayananda Jagannatha

doi:10.1007/978-3-030-32248-9_96

Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage

Vidya Prasad
, Yogish Mallya
, Arun Shastry
, Vijayananda Jagannatha

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

9 Citations (Scopus)

Abstract

Non-contrast head/brain CT (NCHCT) is the initial imaging study of choice in patients visiting any emergency services and could be the only investigation to guide management in patients with head trauma or stroke symptoms. Immediate preliminary radiology reports to trigger appropriate level of care is paramount in the emergency department. Our proposed solution comprises of an efficient method for the detection of intracranial hemorrhage, by creating multiple 2-dimensional (2D) composite images of sub-volumes from the original scan. We also propose a recurrent neural network which combines the sub-volume features and takes into consideration the contextual information across sub-volumes to give a scan level prediction. We achieve an overall AUROC of 0.914.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Editors	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Publisher	Springer
Pages	864-872
Number of pages	9
ISBN (Print)	9783030322472
DOIs	https://doi.org/10.1007/978-3-030-32248-9_96
Publication status	Published - 13 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11766 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Anomaly detection
Composite images
Computed tomography
DCNN
Intracranial hemorrhage
Non-contrast head CT
Recurrent
Sub-volume

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10.1007/978-3-030-32248-9_96

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Prasad, V., Mallya, Y., Shastry, A., & Jagannatha, V. (2019). Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 864-872). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer. https://doi.org/10.1007/978-3-030-32248-9_96

Prasad, Vidya ; Mallya, Yogish ; Shastry, Arun et al. / Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer, 2019. pp. 864-872 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage",

abstract = "Non-contrast head/brain CT (NCHCT) is the initial imaging study of choice in patients visiting any emergency services and could be the only investigation to guide management in patients with head trauma or stroke symptoms. Immediate preliminary radiology reports to trigger appropriate level of care is paramount in the emergency department. Our proposed solution comprises of an efficient method for the detection of intracranial hemorrhage, by creating multiple 2-dimensional (2D) composite images of sub-volumes from the original scan. We also propose a recurrent neural network which combines the sub-volume features and takes into consideration the contextual information across sub-volumes to give a scan level prediction. We achieve an overall AUROC of 0.914.",

keywords = "Anomaly detection, Composite images, Computed tomography, DCNN, Intracranial hemorrhage, Non-contrast head CT, Recurrent, Sub-volume",

author = "Vidya Prasad and Yogish Mallya and Arun Shastry and Vijayananda Jagannatha",

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Prasad, V, Mallya, Y, Shastry, A & Jagannatha, V 2019, Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11766 LNCS, Springer, pp. 864-872. https://doi.org/10.1007/978-3-030-32248-9_96

Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage. / Prasad, Vidya; Mallya, Yogish; Shastry, Arun et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer, 2019. p. 864-872 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS).

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

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T1 - Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage

AU - Prasad, Vidya

AU - Mallya, Yogish

AU - Shastry, Arun

AU - Jagannatha, Vijayananda

PY - 2019/10/13

Y1 - 2019/10/13

N2 - Non-contrast head/brain CT (NCHCT) is the initial imaging study of choice in patients visiting any emergency services and could be the only investigation to guide management in patients with head trauma or stroke symptoms. Immediate preliminary radiology reports to trigger appropriate level of care is paramount in the emergency department. Our proposed solution comprises of an efficient method for the detection of intracranial hemorrhage, by creating multiple 2-dimensional (2D) composite images of sub-volumes from the original scan. We also propose a recurrent neural network which combines the sub-volume features and takes into consideration the contextual information across sub-volumes to give a scan level prediction. We achieve an overall AUROC of 0.914.

AB - Non-contrast head/brain CT (NCHCT) is the initial imaging study of choice in patients visiting any emergency services and could be the only investigation to guide management in patients with head trauma or stroke symptoms. Immediate preliminary radiology reports to trigger appropriate level of care is paramount in the emergency department. Our proposed solution comprises of an efficient method for the detection of intracranial hemorrhage, by creating multiple 2-dimensional (2D) composite images of sub-volumes from the original scan. We also propose a recurrent neural network which combines the sub-volume features and takes into consideration the contextual information across sub-volumes to give a scan level prediction. We achieve an overall AUROC of 0.914.

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KW - Composite images

KW - Computed tomography

KW - DCNN

KW - Intracranial hemorrhage

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SN - 9783030322472

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

SP - 864

EP - 872

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

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A2 - Yap, Pew-Thian

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A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - Springer

ER -

Prasad V, Mallya Y, Shastry A, Jagannatha V. Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage. In Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer. 2019. p. 864-872. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32248-9_96

Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage

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