Automating image analysis by annotating landmarks with deep neural networks

Image and video analysis is often a crucial step in the study of animal behavior and kinematics. Often these analyses require that the position of one or more animal landmarks are annotated (marked) in numerous images. The process of annotating landmarks can require a significant amount of time and tedious labor, which motivates the need for algorithms that can automatically annotate landmarks. In the community of scientists that use image and video analysis to study the 3D flight of animals, there has been a trend of developing more automated approaches for annotating landmarks, yet they fall short of being generally applicable. Inspired by the success of Deep Neural Networks (DNNs) on many problems in the field of computer vision, we investigate how suitable DNNs are for accurate and automatic annotation of landmarks in video datasets representative of those collected by scientists studying animals. Our work shows, through extensive experimentation on videos of hawkmoths, that DNNs are suitable for automatic and accurate landmark localization. In particular, we show that one of our proposed DNNs is more accurate than the current best algorithm for automatic localization of landmarks on hawkmoth videos. Moreover, we demonstrate how these annotations can be used to quantitatively analyze the 3D flight of a hawkmoth. To facilitate the use of DNNs by scientists from many different fields, we provide a self contained explanation of what DNNs are, how they work, and how to apply them to other datasets using the freely available library Caffe and supplemental code that we provide.https://arxiv.org/abs/1702.00583Published versio

Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.Comment: 9 pages, 5 figures, 2 ancillary files; minor changes for camera-ready version. appears in Advances in Neural Information Processing Systems 29 (NIPS 2016

Vision of a Visipedia

The web is not perfect: while text is easily searched and organized, pictures (the vast majority of the bits that one can find online) are not. In order to see how one could improve the web and make pictures first-class citizens of the web, I explore the idea of Visipedia, a visual interface for Wikipedia that is able to answer visual queries and enables experts to contribute and organize visual knowledge. Five distinct groups of humans would interact through Visipedia: users, experts, editors, visual workers, and machine vision scientists. The latter would gradually build automata able to interpret images. I explore some of the technical challenges involved in making Visipedia happen. I argue that Visipedia will likely grow organically, combining state-of-the-art machine vision with human labor

A Survey on Color Normalization Approach to Histopathology Images

The requirement of high-speed data for various forms of application is increasing rapidly. Power Line communication (PLC), a technology which uses the existing power line network as a transmission medium, is a choice for this provision, owing to the ready presence of the medium. This channel (power line), is severely bewitched by noise and attenuation owing to the branches, length and the load connection on the line. Cooperative relaying, which transmits the same information through several nodes is deployed in this paper to combat the data outages caused by the channel's characteristics. Amplify-and-forward and decode-and-forward were the cooperative protocols deployed.The outage probability of each of the protocols were obtained, analysed and compared with the conventional direct link (without cooperation). Results shows that outage probability was drastically reduced on the cooperative links. The performances of the two cooperative links were close due to the noise mitigating circuit incorporated.This achievement in outage probability performance enhances the reliability of the PLC system

The Irrationality of Digital Medicine: VR, IoT and AI in Practice

Artificial Intelligence (AI) has potential to change the tasks that are currently being done by Human being. The Internet of Things (IoT) has potential to increase Medical Device connectivity, enabling more immediate information sharing and coordination between doctors and patients. Virtual reality (VR) has potential to enhance medical training. Digital Health technologies have the power to expand coverage of healthcare (access), enhance services (quality) and reduce/optimize resources (cost). Affordability, infrastructure, and human capability limits overall impacts of Digital Health technologies and must be addressed. Therefore, capacity building through strategic alliances and partnerships remains crucial in harnessing the promises of Digital Health Technologies. This paper summaries challenges in the implementation of Digital health Solutions among African health systems and enumerate the possible ways of addressing the identified challenges. Keywords— Artificial Intelligence (AI), Internet of Things (IoT), Virtual Reality (VR) Digital Health, Remote Patient Monitoring RPM), Robotic Process Automation (RPA

RAD51 as a biomarker for homologous recombination deficiency in high-grade serous ovarian carcinoma: robustness and interobserver variability of the RAD51 test

Biomarker; High-hrade serous ovarian carcinoma; Homologous recombination deficiencyBiomarcador; Carcinoma de ovario seroso de alto grado; Deficiencia de recombinación homólogaBiomarcador; Carcinoma d'ovari serós d'alt grau; Deficiència de recombinació homòlogaThe RAD51 test is emerging as a promising biomarker for the assessment of functional homologous recombination deficiency (HRD). Yet, the robustness and reproducibility of the immunofluorescence-based RAD51 test, in different academic laboratories, have not been systematically investigated. Therefore, we tested the performance of the RAD51 assay in formalin-fixed paraffin-embedded (FFPE) high-grade serous ovarian carcinoma (HGSOC) samples in four European laboratories. Here, we confirm that subtle differences in staining procedures result in low variability of RAD51 and γH2AX scores. However, substantial variability in RAD51 scoring was observed in some samples, likely due to complicating technical and biological features, such as high RAD51 signal-to-noise ratio and RAD51 heterogeneity. These results support the need to identify and perform additional quality control steps and/or automating image analysis. Altogether, resolving technical issues should be a priority, as identifying tumours with functional HRD is urgently needed to guide the individual treatment of HGSOC patients. Follow-up studies are needed to define the key tissue quality requirements to assess HRD by RAD51 in FFPE tumour samples, as this test could help in guiding the individual treatment of HGSOC patients.This work was supported by the Dutch Cancer Society (KWF) (grant: 12995 to TB and MV) and by ERA PerMed (grant ERAPERMED2019-215 to VS). VS received funding from Instituto de Salud Carlos III (CPII19/00033). ALG received funding from Asociación Española Contra el Cáncer (AECC) (INVES20095LLOP) and AHR from Generalitat de Catalunya (PERIS SLT017/20/000081)

Automated quantification of mitral valve anatomy using anatomical intelligence in three-dimensional echocardiography

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Single Cell Analysis of Drug Distribution by Intravital Imaging

Recent advances in the field of intravital imaging have for the first time allowed us to conduct pharmacokinetic and pharmacodynamic studies at the single cell level in live animal models. Due to these advances, there is now a critical need for automated analysis of pharmacokinetic data. To address this, we began by surveying common thresholding methods to determine which would be most appropriate for identifying fluorescently labeled drugs in intravital imaging. We then developed a segmentation algorithm that allows semi-automated analysis of pharmacokinetic data at the single cell level. Ultimately, we were able to show that drug concentrations can indeed be extracted from serial intravital imaging in an automated fashion. We believe that the application of this algorithm will be of value to the analysis of intravital microscopy imaging particularly when imaging drug action at the single cell level

Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors