Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

MRgFUS Uterine Fibroids treatments in Sicily: Preliminary Results and comparison of a Semi-Automatic and manual contouring

Background: Traditional surgery for uterine fibroids treatments (e.g. myomectomy, hysterectomy) offers very invasive therapeutic approaches, which not always preserve reproductive potential of the woman. MRgFUS (MR guided Focused UltraSound) is a new and non-invasive technique for uterine fibroids treatment, not requiring hospitalization and recovery time [1]. On June 2011 and July 2012 the first treatments were started at HSR-Giglio Hospital in Cefalù and at University Hospital (DIBIMEF) in Palermo. An initial assessment of MRgFUS treatment was made by computing the thermally-ablated volume of uterine fibroid. This volume was evaluated considering the NPV (Non Perfused Volume) on a post-treatment MR dataset acquired with contrast medium. Nowadays, the used approach is a time-expensive and operator-dependent manual segmentation procedure. In this paper, the preliminary results of the two Sicilian facilities are showed and a semi- automatic segmentation approach is proposed, based on multi-seed region-growing technique, to calculate the NPV. The realized approach gives a quantitative evaluation in the post-treatment phase. Materials and Methods: The MRgFUS procedures, regarding women affected by single/multiple uterine fibroid, were performed using the ExAblate 2000 and 2100 (InSightec, Haifa, Israel), which is fully integrated with a 1.5 Tesla MR Scanner (GE Medical System, Milwaukee, WI). For NPV manual and semi-automatic evaluation we have used the sagittal or coronal images acquired after the treatment with a gadolinium-based contrast medium (FSPGR+FS+C protocol). The proposed semi-automatic approach in based on multi-seed region-growing, where it is possible to individualize the processing steps: • pre-processing filtering; • region-growing segmentation; • post-processing filtering; • NPV computation. Results: Among all 23 performed treatments, 69.57% have obtained a NPV, manually evaluated, bigger than 60%, successful treatment threshold, and the 65.22% > 70% and 47.83% > 80%. The evaluation of our segmentation approach was performed by calculating Jaccard and Dice similarity indexes and specificity and sensitivity values. In order to obtain the above indexes, the results of the proposed region-growing approach were compared with the manual segmentation. Preliminary segmentation tests obtained the following results: • Jaccard Index: 87.83%; • Dice Index: 94.12%; • Sensitivity: 91.59%; • Specificity: 89.72%. Conclusion: The MRgFUS provides an important new non-invasive and effective treatment for uterine fibroids. The proposed multi-seed region-growing segmentation approach allows to estimate the NPV in a semi-automatic and operator-independent way. Obtained segmentation indexes show the effectiveness of the implemented technique

Endometriosis

Endometriosis is a common and serious disease that is estimated to cost the world economy $9.7 billion a year. Most of these costs come from lost productivity at work. As such, it is important to help women receive earlier diagnosis and more effective treatment. This book presents a comprehensive overview of endometriosis, including information on molecular diagnostics and imaging methods for early detection as well as new, less-invasive treatments that preserve women’s fertility

A semi-automatic multi-seed region-growing approach for uterine fibroids segmentation in MRgFUS treatment

Fibroids are benign tumors growing in the uterus. Most of fibroids do not require treatment unless they are causing symptoms. Traditional surgery treatments, like myomectomy and hysterectomy, are very invasive therapeutic approaches which not always preserves reproductive potential of the woman. MRgFUS, performed with Insightec ExAblate 2100 equipment, is a new and noninvasive technique for uterine fibroids treatment, not requiring hospitalization and recovery time for patients. An initial assessment of MRgFUS treatment is made by computing the ablated volume of uterine fibroid. In this paper a semi-automatic approach, based on region-growing segmentation technique, is proposed. The implemented approach gives a quantitative and qualitative evaluation of the treatment providing the volume and the three-dimensional (3D) model of the treated fibroid area. Considering these characteristics, the proposed approach can be used as a tool to integrate the information used by a Medical Decision Support System (MDSS). As step in the MRgFUS treatment evaluation, the achieved results improve the current methodology based on the manual uterine fibroid ROT segmentation. \ua9 2013 IEEE

Medical-Data-Models.org:A collection of freely available forms (September 2016)

MDM-Portal (Medical Data-Models) is a meta-data repository for creating, analysing, sharing and reusing medical forms, developed by the Institute of Medical Informatics, University of Muenster in Germany. Electronic forms for documentation of patient data are an integral part within the workflow of physicians. A huge amount of data is collected either through routine documentation forms (EHRs) for electronic health records or as case report forms (CRFs) for clinical trials. This raises major scientific challenges for health care, since different health information systems are not necessarily compatible with each other and thus information exchange of structured data is hampered. Software vendors provide a variety of individual documentation forms according to their standard contracts, which function as isolated applications. Furthermore, free availability of those forms is rarely the case. Currently less than 5 % of medical forms are freely accessible. Based on this lack of transparency harmonization of data models in health care is extremely cumbersome, thus work and know-how of completed clinical trials and routine documentation in hospitals are hard to be re-used. The MDM-Portal serves as an infrastructure for academic (non-commercial) medical research to contribute a solution to this problem. It already contains more than 4,000 system-independent forms (CDISC ODM Format, www.cdisc.org, Operational Data Model) with more than 380,000 dataelements. This enables researchers to view, discuss, download and export forms in most common technical formats such as PDF, CSV, Excel, SQL, SPSS, R, etc. A growing user community will lead to a growing database of medical forms. In this matter, we would like to encourage all medical researchers to register and add forms and discuss existing forms