When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

Registration is the process that computes the transformation that aligns sets of data. Commonly, a registration process can be divided into four main steps: target selection, feature extraction, feature matching, and transform computation for the alignment. The accuracy of the result depends on multiple factors, the most significant are the quantity of input data, the presence of noise, outliers and occlusions, the quality of the extracted features, real-time requirements and the type of transformation, especially those ones defined by multiple parameters, like non-rigid deformations. Recent advancements in machine learning could be a turning point in these issues, particularly with the development of deep learning (DL) techniques, which are helping to improve multiple computer vision problems through an abstract understanding of the input data. In this paper, a review of deep learning-based registration methods is presented. We classify the different papers proposing a framework extracted from the traditional registration pipeline to analyse the new learning-based proposal strengths. Deep Registration Networks (DRNs) try to solve the alignment task either replacing part of the traditional pipeline with a network or fully solving the registration problem. The main conclusions extracted are, on the one hand, 1) learning-based registration techniques cannot always be clearly classified in the traditional pipeline. 2) These approaches allow more complex inputs like conceptual models as well as the traditional 3D datasets. 3) In spite of the generality of learning, the current proposals are still ad hoc solutions. Finally, 4) this is a young topic that still requires a large effort to reach general solutions able to cope with the problems that affect traditional approaches.Comment: Submitted to Pattern Recognitio

3D non-rigid registration using color:Color Coherent Point Drift

Research into object deformations using computer vision techniques has been under intense study in recent years. A widely used technique is 3D non-rigid registration to estimate the transformation between two instances of a deforming structure. Despite many previous developments on this topic, it remains a challenging problem. In this paper we propose a novel approach to non-rigid registration combining two data spaces in order to robustly calculate the correspondences and transformation between two data sets. In particular, we use point color as well as 3D location as these are the common outputs of RGB-D cameras. We have propose the Color Coherent Point Drift (CCPD) algorithm (an extension of the CPD method [1]). Evaluation is performed using synthetic and real data. The synthetic data includes easy shapes that allow evaluation of the effect of noise, outliers and missing data. Moreover, an evaluation of realistic figures obtained using Blensor is carried out. Real data acquired using a general purpose Primesense Carmine sensor is used to validate the CCPD for real shapes. For all tests, the proposed method is compared to the original CPD showing better results in registration accuracy in most cases.Comment: Published in Computer Vision and Image Understandin

Translational large animal model of hibernating myocardium: characterization by serial multimodal imaging

Nonrevascularizable coronary artery disease is a frequent cause of hibernating myocardium leading to heart failure (HF). Currently, there is a paucity of therapeutic options for patients with this condition. There is a lack of animal models resembling clinical features of hibernating myocardium. Here we present a large animal model of hibernating myocardium characterized by serial multimodality imaging. Yucatan minipigs underwent a surgical casein ameroid implant around the proximal left anterior descending coronary artery (LAD), resulting in a progressive obstruction of the vessel. Pigs underwent serial multimodality imaging including invasive coronary angiography, cardiac magnetic resonance (CMR), and hybrid 18F-Fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET/CT). A total of 43 pigs were operated on and were followed for 120 ± 37 days with monthly multimodality imaging. 24 pigs (56%) died during the follow-up. Severe LAD luminal stenosis was documented in all survivors. In the group of 19 long-term survivors, 17 (90%) developed left ventricular systolic dysfunction [median LVEF of 35% (IQR 32.5-40.5%)]. In 17/17, at-risk territory was viable on CMR and 14 showed an increased glucose uptake in the at-risk myocardium on 18FDG-PET/CT. The present pig model resembles most of the human hibernated myocardium characteristics and associated heart failure (systolic dysfunction, viable myocardium, and metabolic switch to glucose). This human-like model might be used to test novel interventions for nonrevascularizable coronary artery disease and ischemia heart failure as a previous stage to clinical trials.his study has been partially funded by the Horizon 2020 European Research Area Network on Cardiovascular Diseases (ERA-CVD) Joint Transnational Call “AC16/00021: FAT-4HEART,” by the Spanish Society of Cardiology through a “Translational Research grant 2019,” and by the Instituto de Salud Carlos III (ISCIII) and the European Regional Development Fund (ERDF) through a FIS grant (Ref # PI16/02110). Imaging phenotyping was partially supported by the Comunidad de Madrid (S2017/BMD-3867 RENIM-CM) and cofunded with European structural and investment funds. The CNIC is supported by the ISCIII, the Ministerio de Ciencia e Innovación and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)