Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images

Background: Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g., same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. / Methods: We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). / Results: The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium, and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. / Conclusions: The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task

Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task

Cellulose fibres, nanofibrils and microfibrils: The morphological sequence of MFC components from a plant physiology and fibre technology point of view

During the last decade, major efforts have been made to develop adequate and commercially viable processes for disintegrating cellulose fibres into their structural components. Homogenisation of cellulose fibres has been one of the principal applied procedures. Homogenisation has produced materials which may be inhomogeneous, containing fibres, fibres fragments, fibrillar fines and nanofibrils. The material has been denominated microfibrillated cellulose (MFC). In addition, terms relating to the nano-scale have been given to the MFC material. Several modern and high-tech nano-applications have been envisaged for MFC. However, is MFC a nano-structure? It is concluded that MFC materials may be composed of (1) nanofibrils, (2) fibrillar fines, (3) fibre fragments and (4) fibres. This implies that MFC is not necessarily synonymous with nanofibrils, microfibrils or any other cellulose nano-structure. However, properly produced MFC materials contain nano-structures as a main component, i.e. nanofibrils

Search for supersymmetric particles in scenarios with a gravitino LSP and stau NLSP

Sleptons, neutralinos and charginos were searched for in the context of scenarios where the lightest supersymmetric particle is the gravitino. It was assumed that the stau is the next-to-lightest supersymmetric particle. Data collected with the DELPHI detector at a centre-of-mass energy near 189 GeV were analysed combining the methods developed in previous searches at lower energies. No evidence for the production of these supersymmetric particles was found. Hence, limits were derived at 95% confidence level.Comment: 31 pages, 14 figure

Blood-Feeding Induces Reversible Functional Changes in Flight Muscle Mitochondria of Aedes aegypti Mosquito

Background: Hematophagy poses a challenge to blood-feeding organisms since products of blood digestion can exert cellular deleterious effects. Mitochondria perform multiple roles in cell biology acting as the site of aerobic energytransducing pathways, and also an important source of reactive oxygen species (ROS), modulating redox metabolism. Therefore, regulation of mitochondrial function should be relevant for hematophagous arthropods. Here, we investigated the effects of blood-feeding on flight muscle (FM) mitochondria from the mosquito Aedes aegypti, a vector of dengue and yellow fever. Methodology/Principal Findings: Blood-feeding caused a reversible reduction in mitochondrial oxygen consumption, an event that was parallel to blood digestion. These changes were most intense at 24 h after blood meal (ABM), the peak of blood digestion, when oxygen consumption was inhibited by 68%. Cytochromes c and a+a3 levels and cytochrome c oxidase activity of the electron transport chain were all reduced at 24 h ABM. Ultrastructural and molecular analyses of FM revealed that mitochondria fuse upon blood meal, a condition related to reduced ROS generation. Consistently, BF induced a reversible decrease in mitochondrial H2O2 formation during blood digestion, reaching their lowest values at 24 h ABM where a reduction of 51% was observed. Conclusion: Blood-feeding triggers functional and structural changes in hematophagous insect mitochondria, which may represent an important adaptation to blood feedin