Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-06427-2_16 | IMAGE ANALYSIS AND PROCESSING, ICIAP 2022, PT I |
Keywords | DocType | Volume |
Medical image segmentation, Deep learning, U-Net, Dataset, Chest CT scan, Lung nodules, DeepHealth | Conference | 13231 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hafiza Ayesha Hoor Chaudhry | 1 | 0 | 0.68 |
| Riccardo Renzulli | 2 | 0 | 0.68 |
| Daniele Perlo | 3 | 0 | 1.01 |
| Francesca Santinelli | 4 | 0 | 0.68 |
| Stefano Tibaldi | 5 | 0 | 0.68 |
| Carmen Cristiano | 6 | 0 | 0.68 |
| Marco Grosso | 7 | 0 | 0.68 |
| Giorgio Limerutti | 8 | 0 | 0.34 |
| Attilio Fiandrotti | 9 | 0 | 0.68 |
| Marco Grangetto | 10 | 0 | 0.68 |
| Paolo Fonio | 11 | 0 | 0.34 |