Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammograms to train and validate our model, obtaining an accuracy of 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Deep learning for medical image analysis

Artificial intelligence is a sector characterized by the development of algorithms through which it is possible to analyze complex data, mimicking the human process of learning and applying the acquired information. A sub-category of artificial intelligence is the deep learning. By freeing up the analysis process from pre-processing techniques based on a priori knowledge of the human operator, deep learning allows the automatic extraction of salient information from "raw data" by using a series of levels of representation. This makes it a sector of particular interest for the research world, as consequence of the possibility to identify new information characterizing the data, but at the same time also for the industry world thanks to the ability of analyzing large amounts of data increasingly available and accessible. In few years these methods have revolutionized the computer vision field, thanks also to a class of algorithms that has shown remarkable performance in the analysis of the images, i.e. the convolutional neural networks. The use of these tools has been widely used, not only in the field of natural image analysis, but also in the field of medical imaging for the extraction and quantification of clinical information. In this context, one of the most critical operations, that is carried out within the analysis of medical images, is represented by the segmentation, i.e. the process that leads to semantically separate different structures within an image. This operation can be exploited by us- ing different methods, as there is not a universal algorithm to perform segmentation in medical images. The choice of the best strategy will depend on the image modality, the anatomical region that is going to be considered and from what is defined the clinical task. Given the high variability of competing factors for the choice of the algorithm to be used, in clinical practice manual segmentation strategies or semi-automatic approaches are so used, which however are resolved in time consuming processes and often prone to errors, due to the high dependence on the operator and his degree of experience. Convolutional neural networks allow in this sense to overcome these limits, providing a general and robust method to perform multimodal segmentation operations. In this doctoral thesis, a series of approaches have been developed, based on the use of convolutional neural networks, for the analysis of medical images. In particular, three different types of acquisition were treated, such as computed tomography (CT) acquisitions, with and without contrast medium, magnetic resonance images and mammograms. The cardiac district is considered the area of interest for both CT and magnetic resonance. The CT scans were acquired for the visualization of anatomical and structural information, whilst the magnetic resonance images for functional information studies. All the proposed methods have been designed with the aim of solving a specific medical task, with the development of systems able to extract and quantify automatically clinical information, providing an accurate and fast alternative to manual or semi-automatic solutions. The main contributions of this work on CT images are: the development of a system for the analysis of coronary calcium, a system for the quantification of pericardial fat and a method for the synthesis of data with contrast medium starting from an image that is deprived of it. Regarding magnetic resonance acquisitions, a system for the automatic identification of the left ventricular wall and able to divide it into six segments has been proposed. Finally, a model for the identification of microcalcification clusters was developed for mammographic images. The obtained results have highlighted a high system capability to imitate the expert radiologist work, providing general prediction on new coming data, obtaining reliable measurements and allowing their use for large-scale studies

Sviluppo di un sistema automatico per l'analisi quantitativa 3D di parametri fluidodinamici da acquisizioni 4D-flow MRI

L’imaging di risonanza magnetica rappresenta un importante strumento per la valutazione clinica di pazienti con patologie cardiovascolari. Più recentemente la combinazione della codifica spaziale tridimensionale e delle immagini di phase-contrast codificate secondo la velocità lungo le tre direzioni (4D Flow MRI), è stata stata sviluppata ed applicata per la misura e la visualizzazione dell’evoluzione temporale dell’emodinamica di diversi distretti anatomici, quali aorta, arteria polmonare o carotide. In questo lavoro di tesi viene proposta la progettazione di un sistema automatico, scritto in linguaggio di programmazione python, che preveda l'integrazione di strategie avanzate di preprocessing sulle immagini (ottenute da acquisizioni di risonanza magnetica 4D Flow) con tecniche computazionali inerenti l'interpolazione di dati su strutture ad elementi finiti al fine di ottenere dataset tridimensionali sui quali applicare specifici algoritmi per la stima di parametri fluidodinamici con particolare valenza clinica. La validazione del sistema sviluppato è stata effettuata su dati 4D Flow dell’aorta acquisiti in vivo con scanner di risonanza magnetica a 3 Tesla

Sistemi distribuiti per aste elettroniche: uno studio sperimentale

Lo scopo del mio lavoro di tesi è studiare e discutere la realizzazione mediante un sistema distribuito di un’applicazione per aste olandesi ad alta affidabilità. L’applicazione deve supportare la competizione tra compratori e venditori. Per testare l'affidabilità di tale modello, simuleremo effettivamente il crash di uno o più servers

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

11 pages, 4 figures, submitted to MICCAI 2019 - KiTS ChallengePrecise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The need for accurate and automatic delineation tools is at the origin of the KiTS19 challenge. It aims at accelerating the research and development in this field to aid prognosis and treatment planning by providing a characterized dataset of 300 CT scans to be segmented. To address the challenge, we proposed an automatic, multi-stage, 2.5D deep learning-based segmentation approach based on Residual UNet framework. An ensembling operation is added at the end to combine prediction results from previous stages reducing the variance between single models. Our neural network segmentation algorithm reaches a mean Dice score of 0.96 and 0.74 for kidney and kidney tumors, respectively on 90 unseen test cases. The results obtained are promising and could be improved by incorporating prior knowledge about the benign cysts that regularly lower the tumor segmentation results

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

11 pages, 4 figures, submitted to MICCAI 2019 - KiTS ChallengePrecise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The need for accurate and automatic delineation tools is at the origin of the KiTS19 challenge. It aims at accelerating the research and development in this field to aid prognosis and treatment planning by providing a characterized dataset of 300 CT scans to be segmented. To address the challenge, we proposed an automatic, multi-stage, 2.5D deep learning-based segmentation approach based on Residual UNet framework. An ensembling operation is added at the end to combine prediction results from previous stages reducing the variance between single models. Our neural network segmentation algorithm reaches a mean Dice score of 0.96 and 0.74 for kidney and kidney tumors, respectively on 90 unseen test cases. The results obtained are promising and could be improved by incorporating prior knowledge about the benign cysts that regularly lower the tumor segmentation results