UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans

Hafiza Ayesha Hoor Chaudhry; Riccardo Renzulli; Daniele Perlo; Francesca Santinelli; Stefano Tibaldi; Carmen Cristiano; Marco Grosso; Giorgio Limerutti; Attilio Fiandrotti; Marco Grangetto; Paolo Fonio

doi:10.1007/978-3-031-06427-2_16

UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans

Hafiza Ayesha Hoor Chaudhry, Riccardo Renzulli, Daniele Perlo, Francesca Santinelli, Stefano Tibaldi, Carmen Cristiano, Marco Grosso, Giorgio Limerutti, Attilio Fiandrotti^*, Marco Grangetto, Paolo Fonio

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Lung cancer has emerged as a major causes of death and early detection of lung nodules is the key towards early cancer diagnosis and treatment effectiveness assessment. Deep neural networks achieve outstanding results in tasks such as lung nodules detection, segmentation and classification, however their performance depends on the quality of the training images and on the training procedure. This paper introduces UniToChest, a dataset consisting Computed Tomography (CT) scans of 623 patients. Then, we propose a lung nodules segmentation scheme relying on a convolutional neural architecture that we also re-purpose for a nodule detection task. The experimental results show accurate segmentation of lung nodules across a wide diameter range and better detection accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly made available as a baseline reference.

Original language	English
Title of host publication	Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings
Editors	Stan Sclaroff, Cosimo Distante, Marco Leo, Giovanni M. Farinella, Federico Tombari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	185-196
Number of pages	12
ISBN (Print)	9783031064265
DOIs	https://doi.org/10.1007/978-3-031-06427-2_16
Publication status	Published - 2022
Externally published	Yes
Event	21st International Conference on Image Analysis and Processing, ICIAP 2022 - Lecce, Italy Duration: 23 May 2022 → 27 May 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13231 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Image Analysis and Processing, ICIAP 2022
Country/Territory	Italy
City	Lecce
Period	23/05/22 → 27/05/22

Keywords

Chest CT scan
Dataset
Deep learning
DeepHealth
Lung nodules
Medical image segmentation
U-Net

Access to Document

10.1007/978-3-031-06427-2_16

Cite this

Chaudhry, H. A. H., Renzulli, R., Perlo, D., Santinelli, F., Tibaldi, S., Cristiano, C., Grosso, M., Limerutti, G., Fiandrotti, A., Grangetto, M., & Fonio, P. (2022). UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans. In S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, & F. Tombari (Eds.), Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings (pp. 185-196). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13231 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-06427-2_16

Chaudhry, Hafiza Ayesha Hoor ; Renzulli, Riccardo ; Perlo, Daniele et al. / UniToChest : A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans. Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings. editor / Stan Sclaroff ; Cosimo Distante ; Marco Leo ; Giovanni M. Farinella ; Federico Tombari. Springer Science and Business Media Deutschland GmbH, 2022. pp. 185-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1c8746fd93c54cd68f72036b7d0419cf,

title = "UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans",

abstract = "Lung cancer has emerged as a major causes of death and early detection of lung nodules is the key towards early cancer diagnosis and treatment effectiveness assessment. Deep neural networks achieve outstanding results in tasks such as lung nodules detection, segmentation and classification, however their performance depends on the quality of the training images and on the training procedure. This paper introduces UniToChest, a dataset consisting Computed Tomography (CT) scans of 623 patients. Then, we propose a lung nodules segmentation scheme relying on a convolutional neural architecture that we also re-purpose for a nodule detection task. The experimental results show accurate segmentation of lung nodules across a wide diameter range and better detection accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly made available as a baseline reference.",

keywords = "Chest CT scan, Dataset, Deep learning, DeepHealth, Lung nodules, Medical image segmentation, U-Net",

author = "Chaudhry, {Hafiza Ayesha Hoor} and Riccardo Renzulli and Daniele Perlo and Francesca Santinelli and Stefano Tibaldi and Carmen Cristiano and Marco Grosso and Giorgio Limerutti and Attilio Fiandrotti and Marco Grangetto and Paolo Fonio",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Conference on Image Analysis and Processing, ICIAP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.1007/978-3-031-06427-2_16",

language = "English",

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

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

pages = "185--196",

editor = "Stan Sclaroff and Cosimo Distante and Marco Leo and Farinella, {Giovanni M.} and Federico Tombari",

booktitle = "Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings",

address = "Germany",

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Chaudhry, HAH, Renzulli, R, Perlo, D, Santinelli, F, Tibaldi, S, Cristiano, C, Grosso, M, Limerutti, G, Fiandrotti, A, Grangetto, M & Fonio, P 2022, UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans. in S Sclaroff, C Distante, M Leo, GM Farinella & F Tombari (eds), Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13231 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 185-196, 21st International Conference on Image Analysis and Processing, ICIAP 2022, Lecce, Italy, 23/05/22. https://doi.org/10.1007/978-3-031-06427-2_16

UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans. / Chaudhry, Hafiza Ayesha Hoor; Renzulli, Riccardo; Perlo, Daniele et al.
Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings. ed. / Stan Sclaroff; Cosimo Distante; Marco Leo; Giovanni M. Farinella; Federico Tombari. Springer Science and Business Media Deutschland GmbH, 2022. p. 185-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13231 LNCS).

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

TY - GEN

T1 - UniToChest

T2 - 21st International Conference on Image Analysis and Processing, ICIAP 2022

AU - Chaudhry, Hafiza Ayesha Hoor

AU - Renzulli, Riccardo

AU - Perlo, Daniele

AU - Santinelli, Francesca

AU - Tibaldi, Stefano

AU - Cristiano, Carmen

AU - Grosso, Marco

AU - Limerutti, Giorgio

AU - Fiandrotti, Attilio

AU - Grangetto, Marco

AU - Fonio, Paolo

PY - 2022

Y1 - 2022

N2 - Lung cancer has emerged as a major causes of death and early detection of lung nodules is the key towards early cancer diagnosis and treatment effectiveness assessment. Deep neural networks achieve outstanding results in tasks such as lung nodules detection, segmentation and classification, however their performance depends on the quality of the training images and on the training procedure. This paper introduces UniToChest, a dataset consisting Computed Tomography (CT) scans of 623 patients. Then, we propose a lung nodules segmentation scheme relying on a convolutional neural architecture that we also re-purpose for a nodule detection task. The experimental results show accurate segmentation of lung nodules across a wide diameter range and better detection accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly made available as a baseline reference.

AB - Lung cancer has emerged as a major causes of death and early detection of lung nodules is the key towards early cancer diagnosis and treatment effectiveness assessment. Deep neural networks achieve outstanding results in tasks such as lung nodules detection, segmentation and classification, however their performance depends on the quality of the training images and on the training procedure. This paper introduces UniToChest, a dataset consisting Computed Tomography (CT) scans of 623 patients. Then, we propose a lung nodules segmentation scheme relying on a convolutional neural architecture that we also re-purpose for a nodule detection task. The experimental results show accurate segmentation of lung nodules across a wide diameter range and better detection accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly made available as a baseline reference.

KW - Chest CT scan

KW - Dataset

KW - Deep learning

KW - DeepHealth

KW - Lung nodules

KW - Medical image segmentation

KW - U-Net

UR - http://www.scopus.com/inward/record.url?scp=85130929260&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-06427-2_16

DO - 10.1007/978-3-031-06427-2_16

M3 - Conference contribution

AN - SCOPUS:85130929260

SN - 9783031064265

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

SP - 185

EP - 196

BT - Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings

A2 - Sclaroff, Stan

A2 - Distante, Cosimo

A2 - Leo, Marco

A2 - Farinella, Giovanni M.

A2 - Tombari, Federico

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 23 May 2022 through 27 May 2022

ER -

Chaudhry HAH, Renzulli R, Perlo D, Santinelli F, Tibaldi S, Cristiano C et al. UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans. In Sclaroff S, Distante C, Leo M, Farinella GM, Tombari F, editors, Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 185-196. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-06427-2_16

UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans

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