An end-to-end deep learning approach for landmark detection and matching in medical images

Anatomical landmark correspondences in medical images can provide additional guidance information for the alignment of two images, which, in turn, is crucial for many medical applications. However, manual landmark annotation is labor-intensive. Therefore, we propose an end-to-end deep learning approach to automatically detect landmark correspondences in pairs of two-dimensional (2D) images. Our approach consists of a Siamese neural network, which is trained to identify salient locations in images as landmarks and predict matching probabilities for landmark pairs from two different images. We trained our approach on 2D transverse slices from 168 lower abdominal Computed Tomography (CT) scans. We tested the approach on 22,206 pairs of 2D slices with varying levels of intensity, affine, and elastic transformations. The proposed approach finds an average of 639, 466, and 370 landmark matches per image pair for intensity, affine, and elastic transformations, respectively, with spatial matching errors of at most 1 mm. Further, more than 99% of the landmark pairs are within a spatial

Effectiveness and safety of opicapone in Parkinson's disease patients with motor fluctuations: The OPTIPARK open-label study

BACKGROUND: The efficacy and safety of opicapone, a once-daily catechol-O-methyltransferase inhibitor, have been established in two large randomized, placebo-controlled, multinational pivotal trials. Still, clinical evidence from routine practice is needed to complement the data from the pivotal trials. METHODS: OPTIPARK (NCT02847442) was a prospective, open-label, single-arm trial conducted in Germany and the UK under clinical practice conditions. Patients with Parkinson’s disease and motor fluctuations were treated with opicapone 50 mg for 3 (Germany) or 6 (UK) months in addition to their current levodopa and other antiparkinsonian treatments. The primary endpoint was the Clinician’s Global Impression of Change (CGI-C) after 3 months. Secondary assessments included Patient Global Impressions of Change (PGI-C), the Unified Parkinson’s Disease Rating Scale (UPDRS), Parkinson’s Disease Questionnaire (PDQ-8), and the Non-Motor Symptoms Scale (NMSS). Safety assessments included evaluation of treatment-emergent adverse events (TEAEs) and serious adverse events (SAEs). RESULTS: Of the 506 patients enrolled, 495 (97.8%) took at least one dose of opicapone. Of these, 393 (79.4%) patients completed 3 months of treatment. Overall, 71.3 and 76.9% of patients experienced any improvement on CGI-C and PGI-C after 3 months, respectively (full analysis set). At 6 months, for UK subgroup only (n = 95), 85.3% of patients were judged by investigators as improved since commencing treatment. UPDRS scores at 3 months showed statistically significant improvements in activities of daily living during OFF (mean ± SD change from baseline: − 3.0 ± 4.6, p < 0.0001) and motor scores during ON (− 4.6 ± 8.1, p < 0.0001). The mean ± SD improvements of − 3.4 ± 12.8 points for PDQ-8 and -6.8 ± 19.7 points for NMSS were statistically significant versus baseline (both p < 0.0001). Most of TEAEs (94.8% of events) were of mild or moderate intensity. TEAEs considered to be at least possibly related to opicapone were reported for 45.1% of patients, with dyskinesia (11.5%) and dry mouth (6.5%) being the most frequently reported. Serious TEAEs considered at least possibly related to opicapone were reported for 1.4% of patients. CONCLUSIONS: Opicapone 50 mg was effective and generally well-tolerated in PD patients with motor fluctuations treated in clinical practice. TRIAL REGISTRATION: Registered in July 2016 at clinicaltrials.gov (NCT02847442)

Towards realistic optimization benchmarks: a questionnaire on the properties of real-world problems

Algorithms and the Foundations of Software technolog

Distributed learning to protect privacy in multi-centric clinical studies

Research in medicine has to deal with the growing amount of data about patients which are made available by modern technologies. All these data might be used to support statistical studies, and for identifying causal relations. To use these data, which are spread across hospitals, efficient merging techniques as well as policies to deal with this sensitive information are strongly needed. In this paper we introduce and empirically test a distributed learning approach, to train Support Vector Machines (SVM), that allows to overcome problems related to privacy and data being spread around. The introduced technique allows to train algorithms without sharing any patients-related information, ensuring privacy and avoids the development of merging tools. We tested this approach on a large dataset and we described results, in terms of convergence and performance; we also provide considerations about the features of an IT architecture designed to support distributed learning computations