Artificial Intelligence for chest imaging against COVID-19: an insight into image segmentation methods Buongiorno R., Germanese D., Colligiani L., Fanni S. C., Romei C., Colantonio S. The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2, emerged in late 2019 and soon developed as a pandemic leading to a world health crisis.
Chest imaging examination plays a vital role in the clinical management and prognostic evaluation of COVID-19 since the imaging pathological findings reflect the inflammatory process of the lungs.
Particularly, thanks to its highest sensitivity and resolution, Computer Tomography chest imaging serves well in the distinction of the different parenchymal patterns and manifestations of COVID-19. It is worth noting that detecting and quantifying such manifestations is a key step in evaluating disease impact and tracking its progression or regression over time. Nevertheless, the visual inspection or, even worse, the manual delimitation of such manifestations may be greatly time-consuming and overwhelming for radiologists, especially when pressed by the urgent needs of patient care.
Image segmentation tools, powered by Artificial Intelligence, may sensibly reduce radiologists' workload as they may automate or, at least, facilitate the delineation of the pathological lesions and the other regions of interest for disease assessment. This delineation lays the basis for further diagnostic and prognostic analyses based on quantitative information extracted from the segmented lesions.
This chapter overviews the Artificial Intelligence methods for the segmentation of chest Computed Tomography images. The focus is in particular on Deep Learning approaches, as these have lately become the mainstream approach to image segmentation. A novel method, leveraging attention-based learning, is presented and evaluated. Finally, a discussion of the potential, limitations, and still open challenges of the field concludes the chapter.Source: Artificial Intelligence in Healthcare and COVID-19. Amsterdam: Elsevier, 2022
Data models for an imaging bio-bank for colorectal, prostate and gastric cancer: the NAVIGATOR project Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A. Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022 DOI: 10.1109/bhi56158.2022.9926910 Metrics:
SI-Lab annual research report 2021 Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D. The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022 DOI: 10.32079/isti-ar-2022/003 Metrics:
UIP-net: a decoder-encoder CNN for the detection and quantification of usual interstitial pneumoniae pattern in lung CT scan images Buongiorno R., Germanese D., Romei C., Tavanti L., De Liperi A., Colantonio S. 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.Source: ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges, pp. 389–405, Milan, Italy - Virtual event, 10-15/01/2021 DOI: 10.1007/978-3-030-68763-2_30 Metrics:
SI-Lab Annual Research Report 2020 Leone G. R., Righi M., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D. The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Annual Report, ISTI-2021-AR/001, pp.1–38, 2021 DOI: 10.32079/isti-ar-2021/001 Metrics:
Analisi di immagini tomografiche ad alta risoluzione attraverso reti neurali convoluzionali per lo studio delle interstiziopatie polmonari Buongiorno R., Colantonio S., Germanese D. Le interstiziopatie polmonari (Interstitial Lung Disease, ILD) sono patologie croniche che causano la cicatrizzazione del parenchima polmonare e dell'interstizio alveolare e la compromissione della funzionalità respiratoria. Dal momento che sono più di 200 le patologie raggruppate nella categoria delle ILD, una precisa identificazione è fondamentale per individuare la terapia migliore e formulare una prognosi. L'esame radiologico di riferimento è la tomografia computerizzata del torace ad alta risoluzione (High Resolution Computed Tomography, HRCT) e rappresenta un passaggio cruciale nel processo di diagnosi; nell'analizzare le immagini, infatti, il radiologo deve stabilire se vi è Usual Interstitial Pneumoniae (UIP), ovvero presenza di pattern istopatologici tipici della malattia, e valutarne l'estensione, correlata con la gravità delle alterazioni fisiologiche. Tuttavia, l'incidenza rara delle interstiziopatie fa sì che non tutti i radiologi abbiano un grado di esperienza adatto a individuare visivamente l'anomalia. Inoltre, la malattia si diffonde lungo tutti i polmoni e la segmentazione manuale risulta faticosa.
Nel tentativo di rimediare alla variabilità intra- ed inter-osservatore, sono state sviluppate tecniche per il riconoscimento automatico dei pattern UIP; vi sono approcci basati sull'analisi dell'istogramma e della texture dell'immagine ma, dal momento che i classificatori sono stati addestrati su label definite da operatori clinici diversi, presentano comunque un bias che è causa di identificazioni errate, o mancate, dei pattern.
Il deep learning, invece, si distingue dalle tecniche tradizionali perché fornisce strumenti che imparano autonomamente a classificare i dati. L'obiettivo del lavoro è stato, quindi, progettare e sviluppare la UIP-net, una rete neurale convoluzionale ad-hoc per la segmentazione automatica dei pattern UIP in immagini HRCT di pazienti con Fibrosi Idiopatica Polmonare (IPF), che è una sotto-categoria delle ILD.Source: ISTI Technical Reports 007/2020, 2020, 2020 DOI: 10.32079/isti-tr-2020/007 Metrics:
Analisi di immagini tomografiche ad alta risoluzione attraverso reti neurali convoluzionali per lo studio delle interstiziopatie polmonari Buongiorno R. The term Interstitial Lung Disease (ILD) refers to a large group of lung disorders, most of which cause scars of the interstitium, usually referred to as pulmonary fibrosis. Fibrosis reduces the ability of the air sacs to capture and carry oxygen into the bloodstream, leading to a progressive loss of the ability to breathe. Although ILDs are rare if taken individually, together they represent the most frequent cause of non-obstructive chronic lung disease. Nowadays, there are more than 200 different types of ILDs with varying causes, prognosis and therapies. Thus, identifying the correct type of ILD is necessary to make an accurate diagnosis.
The Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive fibrosing interstitial pneumonia, which is classified among the ILDs with the poorest prognosis. The high variability and unpredictability of IPF course have traditionally made its clinical management hard. The recent introduction of antifibrotic drugs has opened novel therapeutic options for mild to moderate IPF. In this respect, treatment decisions highly rely on the assessment and quantification of IPF impact on the interstitium and its progression over time. High-Resolution Computed Tomography (HRCT) has demonstrated to have a key role in this frame, as it represents a non-invasive diagnostic modality to evaluate and quantify the extent of lung interstitium affected by IPF. In fact, IPF shows a typical radiological pattern, called Usual Interstitial Pneumonia (UIP) pattern, whose presence is usually assessed by radiologists to diagnose IPF. The HRCT features that characterize the UIP pattern are the presence and positioning of specific lung parenchymal anomalies, known as honeycombing , ground-glass opacification and fine reticulation. These anomalies appear in the HRCT scans with specific textural characteristics that are detected via a visual inspection of the imaging data. Assessing the diffusion of these anomalies is instrumental to understand the impact of IPF and to monitor its evolution over time. Quantitative and reliable approaches are in high demand in this respect, as the visual examination by radiologists suffers, by its nature, of poor reproducibility.
To overcome this issue, much research is being conducted to develop new techniques for automatic detection of lung diseases that may support radiologists during the diagnostic pathway, particularly in HRCT image analysis. Indeed, HRCT images evaluation by a Machine Learning (ML)- based algorithm might provide low-cost, reliable, real time automatic identification of UIP pattern with human-level accuracy in order to objectively quantify the percentage of lung volume affected by the disease in a reproducible way.
The purpose of this study was to develop a tool for UIP pattern recognition in HRCT images of patients with IPF using a deep-learning method based on a Convolutional Neural Network (CNN), called UIP-net. UIP-net takes as input a lung HRCT image with 492x492 pixels and outputs the corresponding binary map for the discrimination of disease and normal tissue. To train and evaluate the CNN, a dataset of 5000 images, derived by 20 CT scans from the same scanner, was used. The network performance yielded 83.7% BF-score and 84.6% sensitivity but in order to refine the binary masks produced by UIP-net, a post-processing operation was carried out. With post-processing, vessels, air-ways and tissue wrongly classified as belonging to the lungs were removed from the outputted masks. After post-processing, the results increased to 96.7% BF-score and 85.9% sensitivity. Thus, the network performance, in terms of BF-score and sensitivity, demonstrated that CNNs have the potential to reliably detect disease in order to evaluate its progression and become a supportive tool for radiologists.
Future works include adding more data to the training set in order to add multiple layers to the network to distinguish and quantify the different HRCT features of UIP pattern, improving the reproducibility and reliability of the CNN and using it for the detection of HRCT manifestations of Covid-19.