Deep Mouse: An End-to-end Auto-context Refinement Framework for Brain Ventricle and Body Segmentation in Embryonic Mice Ultrasound Volumes

High-frequency ultrasound (HFU) is well suited for imaging embryonic mice due to its noninvasive and real-time characteristics. However, manual segmentation of the brain ventricles (BVs) and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume around 1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.Comment: Full Paper Submission to ISBI 202

Characterization of brain development in preterm children using ultrasound images

El període més important per al desenvolupament del cervell humà és la fase fetal. Durant aquest període de quaranta setmanes, es produeixen canvis morfològics importants al cervell humà, incloent un enorme augment de la superfície cerebral després del desenvolupament dels solcs i circumvolucions. En els nadons prematurs, aquests canvis es produeixen en un entorn extrauterí i s’ha demostrat un deteriorament del desenvolupament cerebral en aquesta població a una edat equivalent al terme. Un atles normalitzat de maduració cerebral amb ultrasons cerebrals pot permetre als clínics avaluar aquests canvis setmanalment des del naixement fins a una edat equivalent al terme. Basat en les imatges dels diferents nadons proporcionats per dos investigadors clínics, aquest estudi proposa una aplicació web implementada amb Python i les seves diferents biblioteques, inclòs Dash, i accessible a través de Docker que permet accedir directament a l’aplicació dissenyada i a la seva base de dades. D’aquesta manera, es proporciona una eina que permet fer una primera definició de les diferents ranures manualment per passar-les finalment per un algorisme amb l’objectiu de millorar la precisió i poder exportar tant la imatge com les coordenades que se n’obtenen.El período más importante para el desarrollo del cerebro humano es la fase fetal. Durante este período de cuarenta semanas, se producen importantes cambios morfológicos en el cerebro humano, incluido un gran aumento en la superficie del cerebro a raíz del desarrollo de surcos y circunvoluciones. En los recién nacidos prematuros, estos cambios se producen en un entorno extrauterino y se ha demostrado un deterioro del desarrollo cerebral en esta población a la edad equivalente a término. Un atlas normalizado de maduración cerebral con ecografía cerebral puede permitir a los médicos evaluar estos cambios semanalmente desde el nacimiento hasta la edad equivalente a término. A partir de las imágenes de los diferentes bebés proporcionados por dos investigadores clínicos, este estudio propone una aplicación web implementada con Python y sus diferentes bibliotecas, incluida Dash, y accesible a través de Docker que permite el acceso directo a la aplicación diseñada y su base de datos. De esta forma, se proporciona una herramienta que permite realizar una primera definición de las diferentes ranuras de forma manual para finalmente pasarlas por un algoritmo con el objetivo de mejorar la precisión y poder exportar tanto la imagen como las coordenadas obtenidas de la misma.The most important period for human’s brain development is the fetal phase. During these forty weeks period, important morphological changes take place in the human brain, including a huge increase in the brain surface following the development of sulci and gyri. In preterm newborns these changes occur in an extrauterine environment, and an impaired brain development has been shown in this population at term equivalent age. A normalized atlas of brain maturation with cerebral ultrasound may allow the clinicians to assess these changes weekly from birth to term equivalent age. Based on the images of the different babies provided by two clinical researchers, this study proposes a web application implemented with python and its different libraries, including Dash, and accessible through docker that allows direct access to the designed app and its database. In this way, a tool is provided that allows a first definition of the different grooves to be made manually to finally pass them through an algorithm with the aim of improving precision and being able to export both the image and the coordinates obtained from it

Fully Automatic Ultrasound Fetal Heart Image Detection and Segmentation based on Texture Analysis

Ultrasound fetal heart image analysis is important for the antenatal diagnosis of congenital heart disease, therefore, design an automated fetal heart ultrasound image analysis approaches to improve detection ratio of congenital heart disease is necessary. Nevertheless, because of the complicated structure of fetal heart ultrasound image, location, detection and segmentation approaches of fetal heart images as interesting topics that get more attention. Therefore, in this work, we present a framework to segment ultrasound image automatically for tracking the boundary of fetal heart region. In the first step, this paper contributes to breed candidate regions. And then, in the segmentation progress, we apply an energy-based active contour model to detect the edges of fetal heart. Finally, in the experiment section, the performance is estimated by the Dice similarity coefficient, which calculate the spatial overlap between two different segmentation regions, and the experiment results indicate that the proposed algorithm achieves high levels of accuracy

Semi-automatic GUI platform to characterize brain development in preterm children using ultrasound images

The third trimester of pregnancy is the most critical period for human brain development, during which significant changes occur in the morphology of the brain. The development of sulci and gyri allows for a considerable increase in the brain surface. In preterm newborns, these changes occur in an extrauterine environment that may cause a disruption of the normal brain maturation process. We hypothesize that a normalized atlas of brain maturation with cerebral ultrasound images from birth to term equivalent age will help clinicians assess these changes. This work proposes a semi-automatic Graphical User Interface (GUI) platform for segmenting the main cerebral sulci in the clinical setting from ultrasound images. This platform has been obtained from images of a cerebral ultrasound neonatal database images provided by two clinical researchers from the Hospital Sant Joan de Déu in Barcelona, Spain. The primary objective is to provide a user-friendly design platform for clinicians for running and visualizing an atlas of images validated by medical experts. This GUI offers different segmentation approaches and pre-processing tools and is user-friendly and designed for running, visualizing images, and segmenting the principal sulci. The presented results are discussed in detail in this paper, providing an exhaustive analysis of the proposed approach’s effectiveness.Peer ReviewedPostprint (published version

Fully Automated Segmentation of the Left Ventricle in Magnetic Resonance Images

Automatic and robust segmentation of the left ventricle (LV) in magnetic resonance images (MRI) has remained challenging for many decades. With the great success of deep learning in object detection and classification, the research focus of LV segmentation has changed to convolutional neural network (CNN) in recent years. However, LV segmentation is a pixel-level classification problem and its categories are intractable compared to object detection and classification. Although lots of CNN based methods have been proposed for LV segmentation, no robust and reproducible results are achieved yet. In this paper, we try to reproduce the CNN based LV segmentation methods with their disclosed codes and trained CNN models. Not surprisingly, the reproduced results are significantly worse than their claimed accuracies. We also proposed a fully automated LV segmentation method based on slope difference distribution (SDD) threshold selection to compare with the reproduced CNN methods. The proposed method achieved 95.44% DICE score on the test set of automated cardiac diagnosis challenge (ACDC) while the two compared CNN methods achieved 90.28% and 87.13% DICE scores. Our achieved accuracy is also higher than the best accuracy reported in the published literatures. The MATLAB codes of our proposed method are freely available on line

Atrioventricular septal defect : advanced imaging from early development to long-term follow-up

The aim of this thesis is to review the current knowledge on atrioventricular septal defect (AVSD) (Part 1), to study the pathogenesis of AVSD (Part 2) and finally to analyze cardiac outcome long-term after AVSD correction (Part 3). Studies are performed with novel imaging techniques. In part 2 it is made plausible that AVSD is a sliding scale and that patients with Down syndrome without AVSD also have abnormalities of the membranous septum and atrioventricular valves. High frequency ultrasound in mouse embryos shows to be a promising technique to study cardiovascular flow in early stages of heart development. In a mouse model with disturbed VEGF signalling, the heart rate is reduced and the sinoatrial node develops abnormally. Finally, in part 3 of this thesis, 4DFlow MRI data reveals that patients with an abnormal left atrioventricular valve (LAVV) after AVSD correction have aberrant intra-cardiac flow patterns. During diastole the inflow into the left ventricle is directed more towards the lateral wall, more towards the apex and vortex formation is abnormal. During systole the dynamic and eccentric regurgitation of the LAVV disturbs the normal recirculating flow patterns in the left atrium.4DFlow MRI can be used to reliably quantify flow over the LAVV.UBL - phd migration 201

Image analysis platforms for exploring genetic and neuronal mechanisms regulating animal behavior

An important aim of neuroscience is to understand how gene interactions and neuronal networks regulate animal behavior. The larvae of the marine annelid Platynereis dumerilii provide a convenient system for such integrative studies. These larvae exhibit a wide range of behaviors, including phototaxis, chemotaxis and gravitaxis and at the same time exhibit relatively simple nervous system organization. Due to its small size and transparent body, the Platynereis larva is compatible with whole-body light microscopic imaging following tissue staining protocols. It is also suitable for serial electron microscopic imaging and subsequent neuronal connectome reconstruction. Despite advances in imaging techniques, automated computational tools for large data analysis are not well-established in Platynereis. In the current work, I developed image analysis software for exploring genetic and nervous system mechanisms modulating Platynereis behavior. Exploring gene expression patterns Current labeling and imaging techniques restrict the number of gene expression patterns that can be labelled and visualized in a single specimen, which hinders the study of behaviors driven by multi-molecular interactions. To address this problem, I employed image registration to generate a gene expression atlas that integrates gene expression information from multiple specimens in a common reference space. The gene expression atlas was used to investigate mechanisms regulating larval locomotion, settlement and phototaxis in Platynereis. The atlas can assist in the identification of inter-individual and inter-species variations in gene expression. To provide a representation convenient for exploring gene expression patterns, I created a model of the atlas using 3D graphics software, which enabled convenient data visualization and efficient data storage and sharing. Exploring neuronal networks regulating behavior Neuronal circuitry can be reconstructed from the images obtained from electron microscopy, which resolves very fine structures such as neuron morphology or synapses. The amount of data resulting from electron microscopy and the complexity of neuronal networks represent a significant challenge for manual analysis. To solve this problem, I developed the NeuroDetective software, which models a neuronal circuitry and analyzes the information flow within it. The software combines the advantages of 3D visualization and graph analysis software by integrating neuron morphology and spatial distribution together with synaptic connectivity. NeuroDetective allowed studying the neuronal circuitry responsible for phototaxis in Platynereis larvae, revealing the connections and the neurons important for the network functionality. NeuroDetective facilitated the establishment of a relationship between the function and the structure of the neuronal circuitry in Platynereis phototaxis. Integrating gene expression patterns with neuronal connectivity Neuronal circuitry and its associated modulating biomolecules, such as neurotransmitters and neuropeptides, are thought to be the main factors regulating animal behavior. Therefore it was important to integrate both genetic and neuronal information in order to fully understand how biomolecules in conjunction with neuronal anatomy elicit certain animal behavior. To resolve the difference in specimen preparation for gene expression versus electron microscopy preparations, I developed an image registration procedure to match the signals from these two different datasets. This method enabled the integration the spatial distribution of specific modulators into the analysis of neuronal networks, leading to an improved understanding of the genetic and neuronal mechanisms modulating behavior in Platynereis

Zebrafish Models for Development and Disease 2.0

The special issue (Zebrafish Models for Development and Disease 2.0) is a collection of articles highlighting research using the zebrafish (Danio rerio) experimental organism. Research described in this special issue addresses various developmental biology, genetic, biomedical and neuroscience topics that should be of general interest to the biomedical research community