From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer

Tahsin Kurc; Ashish Sharma; Rajarsi Gupta; Le Hou; Han Le; Shahira Abousamra; Erich Bremer; Ryan Birmingham; Tammy DiPrima; Nan Li; Feiqiao Wang; Joseph Balsamo; Whitney Bremer; Dimitris Samaras; Joel Saltz

doi:10.1007/978-3-030-46643-5_37

From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer

Tahsin Kurc
, Ashish Sharma
, Rajarsi Gupta
, Le Hou
, Han Le
, Shahira Abousamra
, Erich Bremer
, Ryan Birmingham
, Tammy DiPrima
, Nan Li
, Feiqiao Wang
, Joseph Balsamo
, Whitney Bremer
, Dimitris Samaras
, Joel Saltz

Emory University
Stony Brook University

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

1 Scopus citations

Abstract

Digital pathology has made great strides in the past decade to create the ability to computationally extract rich information about cancer morphology with traditional image analysis and deep learning. High-resolution whole slide images of cancer tissue samples can be analyzed to quantitatively extract and characterize cellular and sub-cellular phenotypic imaging features. These features combined with genomics and clinical data can be used to advance our understanding of cancer and provide opportunities to the discovery, design, and evaluation of new treatment strategies. Researchers need reliable and efficient image analysis algorithms and software tools that can support indexing, query, and exploration of vast quantities of image analysis data in order to maximize the full potential of digital pathology in cancer research. In this paper we present a brief overview of recent work done by our group, as well as others, in tissue image analysis and digital pathology software systems.

Original language	English
Title of host publication	Brainlesion
Subtitle of host publication	Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
Editors	Alessandro Crimi, Spyridon Bakas
Publisher	Springer
Pages	371-379
Number of pages	9
ISBN (Print)	9783030466428
DOIs	https://doi.org/10.1007/978-3-030-46643-5_37
State	Published - 2020
Event	5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: Oct 17 2019 → Oct 17 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11993 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	10/17/19 → 10/17/19

Keywords

Cancer research
Databases and software tools
Digital pathology
Image analysis
Information technology

Access to Document

10.1007/978-3-030-46643-5_37

Cite this

Kurc, T., Sharma, A., Gupta, R., Hou, L., Le, H., Abousamra, S., Bremer, E., Birmingham, R., DiPrima, T., Li, N., Wang, F., Balsamo, J., Bremer, W., Samaras, D., & Saltz, J. (2020). From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer. In A. Crimi, & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers (pp. 371-379). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11993 LNCS). Springer. https://doi.org/10.1007/978-3-030-46643-5_37

Kurc, Tahsin ; Sharma, Ashish ; Gupta, Rajarsi et al. / From whole slide tissues to knowledge : Mapping sub-cellular morphology of cancer. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. editor / Alessandro Crimi ; Spyridon Bakas. Springer, 2020. pp. 371-379 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{971a13693a38402d982085668b229450,

title = "From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer",

abstract = "Digital pathology has made great strides in the past decade to create the ability to computationally extract rich information about cancer morphology with traditional image analysis and deep learning. High-resolution whole slide images of cancer tissue samples can be analyzed to quantitatively extract and characterize cellular and sub-cellular phenotypic imaging features. These features combined with genomics and clinical data can be used to advance our understanding of cancer and provide opportunities to the discovery, design, and evaluation of new treatment strategies. Researchers need reliable and efficient image analysis algorithms and software tools that can support indexing, query, and exploration of vast quantities of image analysis data in order to maximize the full potential of digital pathology in cancer research. In this paper we present a brief overview of recent work done by our group, as well as others, in tissue image analysis and digital pathology software systems.",

keywords = "Cancer research, Databases and software tools, Digital pathology, Image analysis, Information technology",

author = "Tahsin Kurc and Ashish Sharma and Rajarsi Gupta and Le Hou and Han Le and Shahira Abousamra and Erich Bremer and Ryan Birmingham and Tammy DiPrima and Nan Li and Feiqiao Wang and Joseph Balsamo and Whitney Bremer and Dimitris Samaras and Joel Saltz",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019 ; Conference date: 17-10-2019 Through 17-10-2019",

year = "2020",

doi = "10.1007/978-3-030-46643-5\_37",

language = "English",

isbn = "9783030466428",

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

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}

Kurc, T, Sharma, A, Gupta, R, Hou, L, Le, H, Abousamra, S, Bremer, E, Birmingham, R, DiPrima, T, Li, N, Wang, F, Balsamo, J, Bremer, W, Samaras, D & Saltz, J 2020, From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer. in A Crimi & S Bakas (eds), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11993 LNCS, Springer, pp. 371-379, 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019, Shenzhen, China, 10/17/19. https://doi.org/10.1007/978-3-030-46643-5_37

From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer. / Kurc, Tahsin; Sharma, Ashish; Gupta, Rajarsi et al.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. ed. / Alessandro Crimi; Spyridon Bakas. Springer, 2020. p. 371-379 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11993 LNCS).

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

TY - GEN

T1 - From whole slide tissues to knowledge

T2 - 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019

AU - Kurc, Tahsin

AU - Sharma, Ashish

AU - Gupta, Rajarsi

AU - Hou, Le

AU - Le, Han

AU - Abousamra, Shahira

AU - Bremer, Erich

AU - Birmingham, Ryan

AU - DiPrima, Tammy

AU - Li, Nan

AU - Wang, Feiqiao

AU - Balsamo, Joseph

AU - Bremer, Whitney

AU - Samaras, Dimitris

AU - Saltz, Joel

PY - 2020

Y1 - 2020

N2 - Digital pathology has made great strides in the past decade to create the ability to computationally extract rich information about cancer morphology with traditional image analysis and deep learning. High-resolution whole slide images of cancer tissue samples can be analyzed to quantitatively extract and characterize cellular and sub-cellular phenotypic imaging features. These features combined with genomics and clinical data can be used to advance our understanding of cancer and provide opportunities to the discovery, design, and evaluation of new treatment strategies. Researchers need reliable and efficient image analysis algorithms and software tools that can support indexing, query, and exploration of vast quantities of image analysis data in order to maximize the full potential of digital pathology in cancer research. In this paper we present a brief overview of recent work done by our group, as well as others, in tissue image analysis and digital pathology software systems.

AB - Digital pathology has made great strides in the past decade to create the ability to computationally extract rich information about cancer morphology with traditional image analysis and deep learning. High-resolution whole slide images of cancer tissue samples can be analyzed to quantitatively extract and characterize cellular and sub-cellular phenotypic imaging features. These features combined with genomics and clinical data can be used to advance our understanding of cancer and provide opportunities to the discovery, design, and evaluation of new treatment strategies. Researchers need reliable and efficient image analysis algorithms and software tools that can support indexing, query, and exploration of vast quantities of image analysis data in order to maximize the full potential of digital pathology in cancer research. In this paper we present a brief overview of recent work done by our group, as well as others, in tissue image analysis and digital pathology software systems.

KW - Cancer research

KW - Databases and software tools

KW - Digital pathology

KW - Image analysis

KW - Information technology

UR - https://www.scopus.com/pages/publications/85085529323

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DO - 10.1007/978-3-030-46643-5_37

M3 - Conference contribution

AN - SCOPUS:85085529323

SN - 9783030466428

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

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EP - 379

BT - Brainlesion

A2 - Crimi, Alessandro

A2 - Bakas, Spyridon

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Kurc T, Sharma A, Gupta R, Hou L, Le H, Abousamra S et al. From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer. In Crimi A, Bakas S, editors, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. Springer. 2020. p. 371-379. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-46643-5_37

From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer

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