Tumour classification with optimized sliding window size for OCT imaging: (Conference Poster)

Oskars Čiževskis; Blaž Cugmas; Daira Viškere; Mikus Melderis; Inta Liepniece-Karele; Junjie Yao; Mindaugas Tamošiūnas

doi:10.1117/12.2607185

Tumour classification with optimized sliding window size for OCT imaging: (Conference Poster)

Oskars Čiževskis (Corresponding Author), Blaž Cugmas, Daira Viškere, Mikus Melderis, Inta Liepniece-Karele, Junjie Yao, Mindaugas Tamošiūnas

Department of Pathology

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

1 Citation (Scopus)

Abstract

Skin and subcutaneous tumors are widespread in dogs and cats. Current tumor diagnostics (e.g., biopsy, fine-needle cytology) is invasive and labor-consuming. In this work, we studied ex vivo the most common canine and feline tumor OCT images using sliding window analysis and linear SVC classification, and we compared different sliding window sizes to determine the most optimal window sizes when differentiating between skin, mast cell tumours and soft tissue sarcomas. Sensitivities and specificities of all tissue classes saw an increase with increasing window size at small window size values and plateaued at around 60-80 µm, indicating the most significant tissue structures for differentiation via SWA likely lay here. Our work is the first veterinary OCT study on multiple canine and feline skin tumors to optimize the sliding window size for image pattern analysis.

Original language	English
Title of host publication	Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI
Editors	Joseph A. Izatt, James G. Fujimoto
Place of Publication	San-Francisco
Publisher	SPIE
Volume	XXVI 2022
ISBN (Electronic)	9781510647671
DOIs	https://doi.org/10.1117/12.2607185
Publication status	Published - 2022
Event	Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, 2022 - San Francisco, United States Duration: 24 Jan 2022 → 26 Jan 2022 Conference number: 26 https://www.proceedings.com/spie11948.html

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	11948
ISSN (Print)	1605-7422

Conference

Conference	Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, 2022
Country/Territory	United States
City	San Francisco
Period	24/01/22 → 26/01/22
Internet address	https://www.proceedings.com/spie11948.html

Keywords*

linear discriminant analysis
mast cell tumors
Optical coherence tomography
sliding-window analysis
soft tissue sarcomas
veterinary oncology

Field of Science*

3.2 Clinical medicine

Publication Type*

3.1. Articles or chapters in proceedings/scientific books indexed in Web of Science and/or Scopus database

Access to Document

10.1117/12.2607185

Cite this

Čiževskis, O., Cugmas, B., Viškere, D., Melderis, M., Liepniece-Karele, I., Yao, J., & Tamošiūnas, M. (2022). Tumour classification with optimized sliding window size for OCT imaging: (Conference Poster). In J. A. Izatt, & J. G. Fujimoto (Eds.), Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI (Vol. XXVI 2022). Article 119480S (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11948). SPIE. https://doi.org/10.1117/12.2607185

Čiževskis, Oskars ; Cugmas, Blaž ; Viškere, Daira et al. / Tumour classification with optimized sliding window size for OCT imaging : (Conference Poster). Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI. editor / Joseph A. Izatt ; James G. Fujimoto. Vol. XXVI 2022 San-Francisco : SPIE, 2022. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).

@inproceedings{f3bf3c8de9d94e3abe6bffc0e37b9aa8,

title = "Tumour classification with optimized sliding window size for OCT imaging: (Conference Poster)",

abstract = "Skin and subcutaneous tumors are widespread in dogs and cats. Current tumor diagnostics (e.g., biopsy, fine-needle cytology) is invasive and labor-consuming. In this work, we studied ex vivo the most common canine and feline tumor OCT images using sliding window analysis and linear SVC classification, and we compared different sliding window sizes to determine the most optimal window sizes when differentiating between skin, mast cell tumours and soft tissue sarcomas. Sensitivities and specificities of all tissue classes saw an increase with increasing window size at small window size values and plateaued at around 60-80 µm, indicating the most significant tissue structures for differentiation via SWA likely lay here. Our work is the first veterinary OCT study on multiple canine and feline skin tumors to optimize the sliding window size for image pattern analysis.",

keywords = "linear discriminant analysis, mast cell tumors, Optical coherence tomography, sliding-window analysis, soft tissue sarcomas, veterinary oncology",

author = "Oskars {\v C}i{\v z}evskis and Bla{\v z} Cugmas and Daira Vi{\v s}kere and Mikus Melderis and Inta Liepniece-Karele and Junjie Yao and Mindaugas Tamo{\v s}iūnas",

note = "Funding Information: This research was funded by the Latvian State Education Development Agency (1.1.1.2/VIAA/3/19/455, GoBVM), and the Latvian Council of Science (Lzp-2019/1-0254). Publisher Copyright: {\textcopyright} 2022 SPIE; Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, 2022 ; Conference date: 24-01-2022 Through 26-01-2022",

year = "2022",

doi = "10.1117/12.2607185",

language = "English",

volume = "XXVI 2022",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Izatt, {Joseph A.} and Fujimoto, {James G.}",

booktitle = "Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI",

address = "United States",

url = "https://www.proceedings.com/spie11948.html",

}

Čiževskis, O, Cugmas, B, Viškere, D, Melderis, M, Liepniece-Karele, I, Yao, J & Tamošiūnas, M 2022, Tumour classification with optimized sliding window size for OCT imaging: (Conference Poster). in JA Izatt & JG Fujimoto (eds), Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI. vol. XXVI 2022, 119480S, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11948, SPIE, San-Francisco, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, 2022, San Francisco, United States, 24/01/22. https://doi.org/10.1117/12.2607185

Tumour classification with optimized sliding window size for OCT imaging: (Conference Poster). / Čiževskis, Oskars (Corresponding Author); Cugmas, Blaž; Viškere, Daira et al.
Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI. ed. / Joseph A. Izatt; James G. Fujimoto. Vol. XXVI 2022 San-Francisco: SPIE, 2022. 119480S (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11948).

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

TY - GEN

T1 - Tumour classification with optimized sliding window size for OCT imaging

T2 - Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, 2022

AU - Čiževskis, Oskars

AU - Cugmas, Blaž

AU - Viškere, Daira

AU - Melderis, Mikus

AU - Liepniece-Karele, Inta

AU - Yao, Junjie

AU - Tamošiūnas, Mindaugas

N1 - Conference code: 26

PY - 2022

Y1 - 2022

N2 - Skin and subcutaneous tumors are widespread in dogs and cats. Current tumor diagnostics (e.g., biopsy, fine-needle cytology) is invasive and labor-consuming. In this work, we studied ex vivo the most common canine and feline tumor OCT images using sliding window analysis and linear SVC classification, and we compared different sliding window sizes to determine the most optimal window sizes when differentiating between skin, mast cell tumours and soft tissue sarcomas. Sensitivities and specificities of all tissue classes saw an increase with increasing window size at small window size values and plateaued at around 60-80 µm, indicating the most significant tissue structures for differentiation via SWA likely lay here. Our work is the first veterinary OCT study on multiple canine and feline skin tumors to optimize the sliding window size for image pattern analysis.

AB - Skin and subcutaneous tumors are widespread in dogs and cats. Current tumor diagnostics (e.g., biopsy, fine-needle cytology) is invasive and labor-consuming. In this work, we studied ex vivo the most common canine and feline tumor OCT images using sliding window analysis and linear SVC classification, and we compared different sliding window sizes to determine the most optimal window sizes when differentiating between skin, mast cell tumours and soft tissue sarcomas. Sensitivities and specificities of all tissue classes saw an increase with increasing window size at small window size values and plateaued at around 60-80 µm, indicating the most significant tissue structures for differentiation via SWA likely lay here. Our work is the first veterinary OCT study on multiple canine and feline skin tumors to optimize the sliding window size for image pattern analysis.

KW - linear discriminant analysis

KW - mast cell tumors

KW - Optical coherence tomography

KW - sliding-window analysis

KW - soft tissue sarcomas

KW - veterinary oncology

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DO - 10.1117/12.2607185

M3 - Conference contribution

AN - SCOPUS:85128432497

VL - XXVI 2022

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI

A2 - Izatt, Joseph A.

A2 - Fujimoto, James G.

PB - SPIE

CY - San-Francisco

Y2 - 24 January 2022 through 26 January 2022

ER -

Čiževskis O, Cugmas B, Viškere D, Melderis M, Liepniece-Karele I, Yao J et al. Tumour classification with optimized sliding window size for OCT imaging: (Conference Poster). In Izatt JA, Fujimoto JG, editors, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI. Vol. XXVI 2022. San-Francisco: SPIE. 2022. 119480S. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.2607185

Tumour classification with optimized sliding window size for OCT imaging: (Conference Poster)

Abstract

Publication series

Conference

Keywords*

Field of Science*

Publication Type*

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