UIP-net: a decoder-encoder CNN for the detection and quantification of usual interstitial pneumoniae pattern in lung CT scan images
| Autor: | Annalisa De Liperi, Chiara Romei, Danila Germanese, Rossana Buongiorno, Sara Colantonio, Laura Tavanti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
medicine.diagnostic_test business.industry Convolutional Neural Networks Computed tomography Lung biopsy Idiopatic Pulmonary Fibrosis respiratory system medicine.disease 030218 nuclear medicine & medical imaging respiratory tract diseases Visual recognition 03 medical and health sciences Idiopathic pulmonary fibrosis 0302 clinical medicine Deep Learning 030228 respiratory system Medicine Artificial intelligence Radiology business Encoder |
| Zdroj: | ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges, pp. 389–405, Milan, Italy-Virtual event, 10-15/01/2021 info:cnr-pdr/source/autori:Buongiorno R.; Germanese D.; Romei C.; Tavanti L.; De Liperi A.; Colantonio S./congresso_nome:ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges/congresso_luogo:Milan, Italy-Virtual event/congresso_data:10-15%2F01%2F2021/anno:2021/pagina_da:389/pagina_a:405/intervallo_pagine:389–405 Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687625 ICPR Workshops (1) |
| Popis: | A key step of the diagnosis of Idiopathic Pulmonary Fibrosis (IPF) is the examination of high-resolution computed tomography images (HRCT). IPF exhibits a typical radiological pattern, named Usual Interstitial Pneumoniae (UIP) pattern, which can be detected in non-invasive HRCT investigations, thus avoiding surgical lung biopsy. Unfortunately, the visual recognition and quantification of UIP pattern can be challenging even for experienced radiologists due to the poor inter and intra-reader agreement. This study aimed to develop a tool for the semantic segmentation and the quantification of UIP pattern in patients with IPF using a deep-learning method based on a Convolutional Neural Network (CNN), called UIP-net. The proposed CNN, based on an encoder-decoder architecture, takes as input a thoracic HRCT image and outputs a binary mask for the automatic discrimination between UIP pattern and healthy lung parenchyma. To train and evaluate the CNN, a dataset of 5000 images, derived by 20 CT scans of different patients, was used. The network performance yielded 96.7% BF-score and 85.9% sensitivity. Once trained and tested, the UIP-net was used to obtain the segmentations of other 60 CT scans of different patients to estimate the volume of lungs affected by the UIP pattern. The measurements were compared with those obtained using the reference software for the automatic detection of UIP pattern, named Computer Aided Lungs Informatics for Pathology Evaluation and Rating (CALIPER), through the Bland-Altman plot. The network performance assessed in terms of both BF-score and sensitivity on the test-set and resulting from the comparison with CALIPER demonstrated that CNNs have the potential to reliably detect and quantify pulmonary disease in order to evaluate its progression and become a supportive tool for radiologists. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|