Automatic segmentation of MR brain images with a convolutional neural network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol

On the transport mechanisms, ecological interactions and fate of microplastics in aquatic environments

Microplastics are now persistent throughout aquatic systems globally and can cause a range of ecological damage. The transport of microplastics is influenced by the polymer type, in addition to physical, biological, and chemical gradients plastic particles move through as they are transported across freshwater to marine environments. The combined influence of these mechanisms on microplastic fate is largely unquantified, which prevents identification of accumulation zones and the related development of effective mitigation measures. This research applies a multidisciplinary approach that combines innovative experiments and physical modelling with detailed fieldwork to quantify the main factors influencing microplastic transport and fate. Using a suite of novel settling experiments, biofouling is shown to be a principal factor affecting microplastic deposition through changes in specific density. Yet settling regimes differ depending on polymer and shape as well as ambient sediment and salinity concentrations. To understand particle distribution further, abundances and fluxes of microplastics within a large river system are coupled with hydrological data to explore how microplastics are transported through the vertical water column , finding that most are dispersed below the water surface, yet concentration is dependent on seasonal discharge. Finally, the role of complex coastal ecosystems as a sink for microplastics is investigated in a hydraulic flume under a range of flow conditions. It is shown, for the first time, that microplastic trapping efficiency could be high for both sparse and dense coral canopies due to a reduction in streamwise velocities causing settling on, within, and behind coral structures. It is concluded that although sediment laws can provide a basic understanding of microplastic transport, a new generation of microplastic transport regimes is needed. The findings from the thesis enhance our knowledge of the complex mechanisms that govern microplastic transport and fate in aquatic environments and this new understanding is contextualised in terms of their broad ecological implications

A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully automatic nerve segmentation and morphometric parameter quantification system for corneal confocal microscope images. The segmentation part consists of three main steps. First, a preprocessing step is applied to enhance the visibility of the nerves and remove noise using anisotropic diffusion filtering, specifically a Coherence filter followed by Gaussian filtering. Second, morphological operations are applied to remove unwanted objects in the input image such as epithelial cells and small nerve segments. Finally, an edge detection step is applied to detect all the nerves in the input image. In this step, an efficient algorithm for connecting discontinuous nerves is proposed. In the morphometric parameters quantification part, a number of features are extracted, including thickness, tortuosity and length of nerve, which may be used for the early diagnosis of diabetic polyneuropathy and when planning Laser-Assisted in situ Keratomileusis (LASIK) or Photorefractive keratectomy (PRK). The performance of the proposed segmentation system is evaluated against manually traced ground-truth images based on a database consisting of 498 corneal sub-basal nerve images (238 are normal and 260 are abnormal). In addition, the robustness and efficiency of the proposed system in extracting morphometric features with clinical utility was evaluated in 919 images taken from healthy subjects and diabetic patients with and without neuropathy. We demonstrate rapid (13 seconds/image), robust and effective automated corneal nerve quantification. The proposed system will be deployed as a useful clinical tool to support the expertise of ophthalmologists and save the clinician time in a busy clinical setting

Microplastic trapping efficiency and hydrodynamics in model coral reefs: A physical experimental investigation.

Coastal ecosystems, such as coral reefs, are vulnerable to microplastic pollution input from proximal riverine and shoreline sources. However, deposition, retention, and transport processes are largely unevaluated, especially in relation to hydrodynamics. For the first time, we experimentally investigate the retention of biofilmed microplastic by branching 3D printed corals (staghorn coral Acropora genus) under various unidirectional flows (U = {0.15, 0.20, 0.25, 0.30} ms ) and canopy densities (15 and 48 corals m ). These variables are found to drive trapping efficiency, with 79-98% of microplastics retained in coral canopies across the experimental duration at high flow velocities (U = 0.25-0.30 ms ), compared to 10-13% for the bare bed, with denser canopies retaining only 15% more microplastics than the sparse canopy at highest flow conditions (U = 0.30 ms ). Three fundamental trapping mechanisms were identified: (a) particle interception, (b) settlement on branches or within coral, and (c) accumulation in the downstream wake region of the coral. Corresponding hydrodynamics reveal that microplastic retention and spatial distribution is modulated by the energy-dissipative effects of corals due to flow-structure interactions reducing in-canopy velocities and generating localised turbulence. The wider ecological implications for coral systems are discussed in light of the findings, particularly in terms of concentrations and locations of plastic accumulation. [Abstract copyright: Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.