Un web service per l'utilizzo clinico di un sistema CAD (Computer Aided Detection) per l'analisi automatica di tomografie polmonari

Il lavoro svolto in questa tesi riguarda il test di sistemi CAD (Computer Aided Detection) per la rivelazione automatica di noduli polmonari all'interno di CT (Computed Tomography) polmonari e la realizzazione di un servizio web di CAD on-line

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems

Clinical validation of a web- and cloud-based lung computer aided detection system

The purpose of our work is the clinical validation of a Computer Aided Detection (CAD) system for the automatic identification of pulmonary nodules in chest Computed Tomography (CT) scans. Non-calcified pulmonary nodules are the early manifestation of lung cancers. Lung cancer is the leading cause of cancer-related death worldwide. Screening high risk individuals for lung cancer with low-dose CT scans is now being implemented in the United States and other countries are expected to follow soon. The detection of these pathological Regions Of Interest (ROIs) is a burden task for radiologists, mainly due to the high number of noisy images to be analysed. To support radiologists, researchers have started implementing CAD algorithms for the automatic identification of pathological ROIs. Several studies proved the positive impact of CADs as a support for radiologists in the detection, with sensitively benefit on the overall performance. Despite these very prominent results, CAD systems have not been spread in clinical routine yet. In fact, the standard approach to make CAD algorithms available in the clinical routine of health facilities, that is the deployment of standalone workstations, usually equipped with a vendor-dependent Graphic User Interface (GUI), presents several drawbacks, such as the high fixed cost of the software licenses and the dedicated hardware and the rapid obsolescence of both. Furthermore, the computational needs by CAD algorithms can be demanding, depending on their complexity, often requiring powerful and expensive hardware. The diffusion of Cloud Computing solutions, accessible via secure Web protocols, solves almost all the previous two issues. In addition, the Software as A Service (SaaS) approach provides the possibility of combining several CADs, with demonstrated benefits to the overall performance

A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung Nodules in Thoracic CTs (Computed Tomographies)

M5L, a Web-based Computer-Aided Detection (CAD) system to automatically detect lung nodules in thoracic Computed Tomographies, is based on a multi-thread analysis by independent subsystems and the combination of their results. The validation on 1043 scans of 3 independent data-sets showed consistency across data-sets, with a sensitivity of about 80% in the 4-8 range of False Positives per scan, despite varying acquisition and reconstruction parameters and annotation criteria. To make M5L CAD available to users without hardware or software new installations and configuration, a Software as a Service (SaaS) approach was adopted. A web front-end handles the work (image upload, results notification and direct on-line annotation by radiologists) and the communication with the OpenNebula-based cloud infrastructure, that allocates virtual computing and storage resources. The exams uploaded through the web interface are anonymised and analysis is performed in an isolated and independent cloud environment. The average processing time for case is about 20 minutes and up to 14 cases can be processed in parallel. Preliminary results on the on-going clinical validation shows that the M5L CAD adds 20% more nodules originally overlooked by radiologists, allowing a remarkable increase of the overall detection sensitivity

A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies

Objectives: To compare unassisted and CAD-assisted detection and time efficiency of radiologists in reporting lung nodules on CT scans taken from patients with extra-thoracic malignancies using a Cloud-based system. Materials and methods: Three radiologists searched for pulmonary nodules in patients with extra-thoracic malignancy who underwent CT (slice thickness/spacing 2 mm/1.7 mm) between September 2015 and March 2016. All nodules detected by unassisted reading were measured and coordinates were uploaded on a cloud-based system. CAD marks were then reviewed by the same readers using the cloud-based interface. To establish the reference standard all nodules ≥ 3 mm detected by at least one radiologist were validated by two additional experienced radiologists in consensus. Reader detection rate and reporting time with and without CAD were compared. The study was approved by the local ethics committee. All patients signed written informed consent. Results: The series included 225 patients (age range 21–90 years, mean 62 years), including 75 patients having at least one nodule, for a total of 215 nodules. Stand-alone CAD sensitivity for lesions ≥ 3 mm was 85% (183/215, 95% CI: 82–91); mean false-positive rate per scan was 3.8. Sensitivity across readers in detecting lesions ≥ 3 mm was statistically higher using CAD: 65% (95% CI: 61–69) versus 88% (95% CI: 86–91, p<0.01). Reading time increased by 11% using CAD (296 s vs. 329 s; p<0.05). Conclusion: In patients with extra-thoracic malignancies, CAD-assisted reading improves detection of ≥ 3-mm lung nodules on CT, slightly increasing reading time. Key Points: • CAD-assisted reading improves the detection of lung nodules compared with unassisted reading on CT scans of patients with primary extra-thoracic tumour, slightly increasing reading time. • Cloud-based CAD systems may represent a cost-effective solution since CAD results can be reviewed while a separated cloud back-end is taking care of computations. • Early identification of lung nodules by CAD-assisted interpretation of CT scans in patients with extra-thoracic primary tumours is of paramount importance as it could anticipate surgery and extend patient life expectancy

Clinical validation of the M5L lung computer-assisted detection system

Lung cancer is one of the leading causes of death in the world. Early diagnosis is crucial to limit mortality. In order to support radiologists in the diagnosis several Computer-Assisted Detection (CAD) systems were develope

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD system

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD system