Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of synapses between neurons. As manual extraction of this information is very time-consuming, there has been extensive research effort to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively less research on automatically detecting the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections.
However, recent research has demonstrated that contact area alone is not a sufficient predictor of synaptic connection. Moreover, as segmentation has improved, we have observed that synapse annotation is consuming a more significant fraction of overall reconstruction time. This ratio will only get worse as segmentation improves, gating overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect pre-synaptic neurons and their post-synaptic partners. In particular, pre-synaptic structures are detected using a Deep and Wide Multiscale Recursive Network, and post-synaptic partners are detected using a MLP with features conditioned on the local segmentation.
This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. These metrics demonstrate that complete automatic prediction can be used to effectively characterize most connectivity correctly.
A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visu…
Harvard University（哈佛大学）；Max Planck Institute for Brain Research（马克斯·普朗克大脑研究所）
Synaptic connectivity detection is a critical task for neural reconstruction from Electron Microscopy (EM) data. Most of the existing algorithms for synapse detection do not identify the cleft location and direction of connectivity simultaneously. The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types. In this paper, we present an algorithm to automatically predict the location as well as the direction of both dyadic and poly…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Electron tomography (ET) allows high-resolution reconstructions of macromolecular complexes at nearnative state. Cellular structures segmentation in the reconstruction data from electron tomographic images is often required for analyzing and visualizing biological structures, making it a powerful tool for quantitative descriptions of whole cell structures and understanding biological functions. However, these cellular structures are rather difficult to automatically separate or quantify from view owing to complex molecular environment and the limitations of reconstruction data of ET. In this p…
Identifying complex neural circuitry from electron microscopic (EM) images may help unlock the mysteries of the brain. However, identifying this circuitry requires time-consuming, manual tracing (proofreading) due to the size and intricacy of these image datasets, thus limiting state-of-the-art analysis to very small brain regions. Potential avenues to improve scalability include automatic image segmentation and crowd sourcing, but current efforts have had limited success. In this paper, we propose a new strategy, focused proofreading, that works with automatic segmentation and aims to limit p…
Brain image segmentation is used for visualizing and quantifying anatomical structures of the brain. We present an automated ap-proach using 2D deep residual dilated networks which captures rich context information of different tissues for the segmentation of eight brain structures. The proposed system was evaluated in the MICCAI Brain Segmentation Challenge and ranked 9th out of 22 teams. We further compared the method with traditional U-Net using leave-one-subject-out cross-validation setting on the public dataset. Experimental results shows that the proposed method outperforms traditional U…
Inria（法国国家信息与自动化研究所）；IHU Liryc（利里克大学医院研究所）；University of Bordeaux（波尔多大学）
Radiological imaging offers effective measurement of anatomy, which is useful in disease diagnosis and assessment. Previous study has shown that the left atrial wall remodeling can provide information to predict treatment outcome in atrial fibrillation. Nevertheless, the segmentation of the left atrial structures from medical images is still very time-consuming. Current advances in neural network may help creating automatic segmentation models that reduce the workload for clinicians. In this preliminary study, we propose automated, two-stage, three-dimensional U-Nets with convolutional neural …
Universidad de La Rioja（拉里奥哈大学）；Instituto de Neurociencias（神经科学研究所）；Universidad Autónoma de Barcelona（巴塞罗那自治大学）
The quantification of synapses is instrumental to measure the evolution of synaptic densities of neurons under the effect of some physiological conditions, neuronal diseases or even drug treatments. However, the manual quantification of synapses is a tedious, error-prone, time-consuming and subjective task; therefore, tools that might automate this process are desirable. In this paper, we present SynapCountJ, an ImageJ plugin, that can measure synaptic density of individual neurons obtained by immunofluorescence techniques, and also can be applied for batch processing of neurons that have been…
This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary pose estimation algorithm, and returns a binary label indicating the validity of the result. We demonstrate that our approach improves upon a state-of-the-art pose estimation result on the Siléane dataset, outperforming a variation of the alternative CullNet method by 4.15% in average class accuracy and 0.73% in overall accuracy at validation. Applyi…
University of Naples Federico II（那不勒斯费德里科二世大学）；University of Maryland（马里兰大学）；University of Salzburg（萨尔茨堡大学）
The European Space Agency (ESA) defines an Earth Observation (EO) Level 2 product as a multispectral (MS) image corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its scene classification map (SCM) whose legend includes quality layers such as cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To contribute toward filling an information gap from EO big sensory data to the ESA EO Level 2 product, a Stage 4 validation (Val) of an off the shelf Satellite Image Automatic Mapper (SIAM) lightweight compute…
This paper summarizes our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation
SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions
Department of Biomedical Engineering, Bangladesh University of Engineering and Technology（生物医学工程系，孟加拉国工程与技术大学）；Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology（计算机科学与工程系，孟加拉国工程与技术大学）；Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology（电气与电子工程系，孟加拉国工程与技术大学）
This paper summarizes our method and validation results for the ISBI Challenge 2017 - Skin Lesion Analysis Towards Melanoma Detection - Part I: Lesion Segmentation
University of Naples Federico II（那不勒斯费德里科二世大学）；University of Maryland（马里兰大学）；University of Salzburg（萨尔茨堡大学）
The European Space Agency (ESA) defines an Earth Observation (EO) Level 2 product as a multispectral (MS) image corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its scene classification map (SCM), whose legend includes quality layers such as cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To contribute toward filling an information gap from EO big data to the ESA EO Level 2 product, an original Stage 4 validation (Val) of the Satellite Image Automatic Mapper (SIAM) lightweight computer program …
Radboud University Medical Center（拉德堡德大学医学中心）；Polytechnic University of Turin（都灵理工大学）；Istituto Nazionale di Fisica Nucleare（国家核物理研究院）；Radboud University Nijmegen（拉德堡德大学）；Radboud University（拉德堡德大学）；The Chinese University of Hong Kong（香港中文大学）；University of Pisa（比萨大学）
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in o…
Institute for Infocomm Research（资讯通信研究院）；A*STAR（新加坡科技研究局）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；University of Electronic Science and Technology of China（电子科技大学）
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics. In this work, we instead propose to automatically learn a data augmentation strategy using bilevel optimization. An augmentor is designed in a similar fashion to a conditional generator and is optimized by minimizing a base model's loss on a validation set when the augmented input is used for training the model. This formulation provides a more principled way to learn data augmentation on 3D point clouds. We…
In recent years, large "in the wild" face datasets have been released in an attempt to facilitate progress in tasks such as face detection, face recognition, and other tasks. Most of these datasets are acquired from webpages with automatic procedures. As a consequence, noisy data are often found. Furthermore, in these large face datasets, the annotation of identities is important as they are used for training face recognition algorithms. But due to the automatic way of gathering these datasets and due to their large size, many identities folder contain mislabeled samples which deteriorates the…
Shenzhen Deepcam Information Technologies（深圳深图信息技术有限公司）；LinkSprite Technologies（联芯通科技）
We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice images. In this framework, the slice images of a CT-scan volume are first proprocessed using segmentation techniques to filter out images of closed lung, and to remove the useless background. Then a resampling method is used to select one or multiple sets of a fixed number of slice images for training and validation. A 3D CNN network with BERT is used to classify this set of selected slice images. In this network, an embedding feature is also extracted. In cases where there are more than one set of slice images in a …
Uppsala University（乌普萨拉大学）；SEED - Electronic Arts (EA)（SEED——美国艺电（EA））
Modern video games are rapidly growing in size and scale, and to create rich and interesting environments, a large amount of content is needed. As a consequence, often several thousands of detailed 3D assets are used to create a single scene. As each asset's polygon mesh can contain millions of polygons, the number of polygons that need to be drawn every frame may exceed several billions. Therefore, the computational resources often limit how many detailed objects that can be displayed in a scene. To push this limit and to optimize performance one can reduce the polygon count of the assets whe…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
In this paper, we use a fully convolutional neural network (FCNN) for the segmentation of gliomas from Magnetic Resonance Images (MRI). A fully automatic, voxel based classification was achieved by training a 23 layer deep FCNN on 2-D slices extracted from patient volumes. The network was trained on slices extracted from 130 patients and validated on 50 patients. For the task of survival prediction, texture and shape based features were extracted from T1 post contrast volume to train an XGBoost regressor. On BraTS 2017 validation set, the proposed scheme achieved a mean whole tumor, tumor core…
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Mannheim University of Applied Science（曼海姆应用科学大学）
Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and non-reproducible outcomes. In this paper, we present a method that addresses named limitations by integrating segmentation and disease classification into a fully automatic processing pipeline. We use an ensemble of UNet inspired architectures for segmentation of cardiac structures such as the left and right ventricular cavity (LVC, RVC) and the left ventricular myocardium…
A Fully Automatic Framework for Intracranial Pressure Grading: Integrating Keyframe Identification, ONSD Measurement and Clinical Data
School of Mathematics, Nanjing University, Nanjing, 210093, China（南京大学数学系）；Department of Geriatric, Nanjing Drum Tower Hospital Affiliated of Nanjing University Medical School, Nanjing, 210008, China（南京大学医学院附属鼓楼医院老年科）；Drum Tower Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, China（南京中医药大学鼓楼临床医学院）；Department of Intensive Medicine, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, 211100, China（南京医科大学附属江宁医院重症医学科）
Biomediq A/S（Biomediq公司）；University of Copenhagen（哥本哈根大学）
Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic image analysis methods used in longitudinal studies. The implication is increased measurement variation and a risk of bias in the estimations (e.g. in the volume change for a structure). We proposed two quite different approaches for scanner drift normalization and demonstrated the performance for segm…
Diffusion-Weighted Magnetic Resonance Imaging (DWI) is widely used for early cerebral infarct detection caused by ischemic stroke. Manual segmentation is done by a radiologist as a common clinical process, nonetheless, challenges of cerebral infarct segmentation come from low resolution and uncertain boundaries. Many segmentation techniques have been proposed and proved by manual segmentation as gold standard. In order to reduce human error in research operation and clinical process, we adopt a semi-automatic segmentation as gold standard using Fluid-Attenuated Inversion-Recovery (FLAIR) Magne…
CONICET（阿根廷国家科学与技术研究理事会）；The Bariloche Atomic Center（巴里洛切原子中心）；Instituto Balseiro（巴尔塞罗研究所）；Universidad Nacional de Cuyo（库约国立大学）；Sanatorio San Carlos（圣卡洛斯疗养院）；National Atomic Energy Commission (Comisión Nacional de Energía Atómica, CNEA)（国家原子能委员会（CNEA））
Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A tot…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Wake Forest School of Medicine（维克森林大学医学院）
Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD). However, manual tracing is resource intensive. To address these limitations and to expand the availability of a quantitative CT measure of hepatic steatosis, we propose the automatic liver attenuation ROI-based measurement (ALARM) method for automated liver attenuation estimation. The ALARM method consists of two major stages: (1) deep convolutional neural network (DCNN)-based liver segme…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Intel Technology India Pvt. Ltd（英特尔技术印度私人有限公司）
We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connectivity patterns to reduce the number of weights and residual connections and is initialized with weights obtained from training this model with BraTS 2018 dataset. Hard mining is done during training to train for the difficult cases of segmentation tasks by increasing the dice similarity coefficient (DSC) threshold to choose the hard cases as epoch increases. On the BraTS2020 validation data (n = 125), this architect…
AI Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学AI·维尔塔宁分子科学研究所）；Charles River Discovery Services（查尔斯河发现服务公司）；Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi（费米中心——恩里科·费米物理历史博物馆与研究中心）
Manual segmentation of rodent brain lesions from magnetic resonance images (MRIs) is an arduous, time-consuming and subjective task that is highly important in pre-clinical research. Several automatic methods have been developed for different human brain MRI segmentation, but little research has targeted automatic rodent lesion segmentation. The existing tools for performing automatic lesion segmentation in rodents are constrained by strict assumptions about the data. Deep learning has been successfully used for medical image segmentation. However, there has not been any deep learning approach…
New Jersey Institute of Technology（新泽西理工学院）；Kessler Foundation（凯斯勒基金会）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；Medical College of Wisconsin（威斯康星医学院）；Rutgers University - Newark（罗格斯大学纽瓦克分校）
We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images. Our multi-path model has independent encoders for different modalities containing residual convolutional blocks, weighted multi-path feature fusion from different modalities, and weighted fusion modules to combine encoder and decoder features. Compared to existing 3D CNNs like DeepMedic, 3D U-Net, and AnatomyNet, our networks achieves the highest statistically significant cross-validation accuracy of 60.5% on the large ATLAS benchmark of 220 patients. We also test our model on multi-modal…
LinkSprite Technologies（LinkSprite科技公司）；Shenzhen Deepcam Information Technologies（深圳深视信息科技公司）
We present an automatic COVID1-19 diagnosis framework from lung CT images. The focus is on signal processing and classification on small datasets with efforts putting into exploring data preparation and augmentation to improve the generalization capability of the 2D CNN classification models. We propose a unique and effective data augmentation method using multiple Hounsfield Unit (HU) normalization windows. In addition, the original slice image is cropped to exclude background, and a filter is applied to filter out closed-lung images. For the classification network, we choose to use 2D Densen…
University of Coimbra（科英布拉大学）；Erasmus MC（伊拉斯姆斯大学医学中心）；KU Leuven（鲁汶大学）；University of Porto（波尔图大学）；Centro Hospitalar e Universitário São João（圣若昂大学医院中心）；University Hospitals UZ Leuven（鲁汶大学医院）
The optic nerve head represents the intraocular section of the optic nerve (ONH), which is prone to damage by intraocular pressure. The advent of optical coherence tomography (OCT) has enabled the evaluation of novel optic nerve head parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane opening minimum-rim-width, these seem to be promising optic nerve head parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these optical coherence tomography derived biomarkers are mostly extracted through manual segment…
