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.

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

Chaudhry H. A. H.;Renzulli R.;Perlo D.;Fiandrotti A.;Grangetto M.;Fonio P.
2022

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.
21st International Conference on Image Analysis and Processing, ICIAP 2022
ita
2022
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Springer Science and Business Media Deutschland GmbH
13231
185
196
978-3-031-06426-5
978-3-031-06427-2
https://link.springer.com/chapter/10.1007/978-3-031-06427-2_16
Chest CT scan; Dataset; Deep learning; DeepHealth; Lung nodules; Medical image segmentation; U-Net
Chaudhry H.A.H.; Renzulli R.; Perlo D.; Santinelli F.; Tibaldi S.; Cristiano Carmen; Grosso M.; Limerutti G.; Fiandrotti A.; Grangetto M.; Fonio P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1863525
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