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discovering Functional Brain Networks with 3d Residual Autoencoder (ResAE) 23rd

Discovering Functional Brain Networks with 3D Residual Autoe...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： dong, Qinglin Qiang, Ning Lv, Jinglei Li, Xiang Liu, Tianming Li, Quanzheng Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA United States School of Physics and Information Technology Shaanxi Normal University Xian China School of Biomedical Engineering and Sydney Imaging Brain and Mind Centre The University of Sydney Camperdown Australia Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States MGH & BWH Center for Clinical Data Science BostonMA United States

ISBN: (纸本)9783030597276

Functional MRI has attracted increasing attention in cognitive neuroscience and clinical mental health research. Towards understanding how brain give rises to mental phenomena, deep learning has been applied to functional MRI (fMRI) dataset to discover the physiological basis of cognitive process. Considering the unsupervised nature of fMRI due to the complex intrinsic brain activities, an encoder-decoder structure is promising to model hidden structure of latent signal sources. Inspired by the success of deep residual learning, we propose a 68-layer 3d residual autoencoder (3d ResAE) to model deep representations of fMRI in this paper. The proposed model is evaluated on the fMRI data under 3 cognitive tasks in Human Connectome Project (HCP). The experimental results have shown that the temporal representations learned by the encoder matches the task design and the spatial representations can be interpreted to be meaningful functional brain networks (FBNs), which not only include tasks based FBNs, but also intrinsic FBNs. The proposed model also outperforms a 3-layer autoencoder, showing the key factor for the performance improvement is depth. Our work demonstrates the feasibility and success of adopting 2d advanced deep residual networks in computer vision into 3d fMRI volume modeling. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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MitoEM dataset: Large-Scale 3d Mitochondria Instance Segmentation from EM Images 23rd

MitoEM Dataset: Large-Scale 3D Mitochondria Instance Segment...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Wei, donglai Lin, Zudi Franco-Barranco, daniel Wendt, Nils Liu, Xingyu Yin, Wenjie Huang, Xin Gupta, Aarush Jang, Won-dong Wang, Xueying Arganda-Carreras, Ignacio Lichtman, Jeff W. Pfister, Hanspeter Harvard University Cambridge United States Donostia International Physics Center Donostia Spain University of the Basque Country Leioa Spain Technical University of Munich Munich Germany Shanghai Jiao Tong University Shanghai China Northeastern University Boston United States Indian Institute of Technology Roorkee Roorkee India Ikerbasque Basque Foundation for Science Bilbao Spain

ISBN: (纸本)9783030597214

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. However, public mitochondria segmentation datasets only contain hundreds of instances with simple shapes. It is unclear if existing methods achieving human-level accuracy on these small datasets are robust in practice. To this end, we introduce the MitoEM dataset, a 3d mitochondria instance segmentation dataset with two (30μ m)3 volumes from human and rat cortices respectively, 3,600 larger than previous benchmarks. With around 40K instances, we find a great diversity of mitochondria in terms of shape and density. For evaluation, we tailor the implementation of the average precision (AP) metric for 3d data with a 45 speedup. On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances. Thus, our MitoEM dataset poses new challenges to the field. We release our code and data: https://***/page/mitoEM/***. © 2020, Springer Nature Switzerland AG.

关键词： Large dataset

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Inkjet 3d-printed microcantilever nanogram resolution mass sensor 23

Inkjet 3d-printed microcantilever nanogram resolution mass s...

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23rd international conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019

作者： Sniadek, Patrycja Kawa, Bartosz Walczak, Rafal Technical Wroclaw University of Science and Technology Faculty of Microsystem Electronics and Photonics Poland

ISBN: (纸本)9781733419000

We used for the first time an inkjet 3d printed vibrational mass sensor for submillimeter objects mass measurement with nanograms resolution. The 3d printed microcantilever structure allowed to determine mass of a glass microbead with known mass ~100-450 ng. Optical detection system allowed for measurement of frequency with resolution 0,1 Hz. Increase of the resonant frequency with microbead load was observed with a decrease of mass of the microbeads. In the future such mass sensor can be used for determinate mass of biological cells. © 2019 CBMS-0001.

关键词： 3d printers

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Isotropic Reconstruction of 3d EM Images with Unsupervised degradation Learning 23rd

Isotropic Reconstruction of 3D EM Images with Unsupervised D...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： deng, Shiyu Fu, Xueyang Xiong, Zhiwei Chen, Chang Liu, dong Chen, Xuejin Ling, Qing Wu, Feng University of Science and Technology of China Hefei China Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030597214

The isotropic reconstruction of 3d electron microscopy (EM) images with low axial resolution is of great importance for biological analysis. Existing deep learning-based methods rely on handcrafted down-scaled training data, which does not model the real degradation accurately and thus leads to unsatisfying performance in practice. To address this problem, we propose a universal and unsupervised framework to simultaneously learn the real axial degradation and the isotropic reconstruction of 3d EM images. First, we train a degradation network using unpaired low-resolution (LR) and high-resolution (HR) slices, both of which are from real data, in an adversarial manner. Then, the degradation network is further used to generate realistic LR data from HR labels to form paired training data. In this way, the generated degraded data is consistent with the real axial degradation process, which guarantees the generalization ability of subsequent reconstruction networks to the real data. Our framework has the flexibility to work with different existing reconstruction methods. Experiments on both simulated and real anisotropic EM images validate the superiority of our framework. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

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deU-Net: deformable U-Net for 3d Cardiac MRI Video Segmentation 23rd

DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentat...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： dong, Shunjie Zhao, Jinlong Zhang, Maojun Shi, Zhengxue deng, Jianing Shi, Yiyu Tian, Mei Zhuo, Cheng Zhejiang University Hangzhou China University of Notre Dame Notre Dame United States

ISBN: (纸本)9783030597184

Automatic segmentation of cardiac magnetic resonance imaging (MRI) facilitates efficient and accurate volume measurement in clinical applications. However, due to anisotropic resolution and ambiguous border (e.g., right ventricular endocardium), existing methods suffer from the degradation of accuracy and robustness in 3d cardiac MRI video segmentation. In this paper, we propose a novel deformable U-Net (deU-Net) to fully exploit spatio-temporal information from 3d cardiac MRI video, including a Temporal deformable Aggregation Module (TdAM) and a deformable Global Position Attention (dGPA) network. First, the TdAM takes a cardiac MRI video clip as input with temporal information extracted by an offset prediction network. Then we fuse extracted temporal information via a temporal aggregation deformable convolution to produce fused feature maps. Furthermore, to aggregate meaningful features, we devise the dGPA network by employing deformable attention U-Net, which can encode a wider range of multi-dimensional contextual information into global and local features. Experimental results show that our deU-Net achieves the state-of-the-art performance on commonly used evaluation metrics, especially for cardiac marginal information (ASSd and Hd). © 2020, Springer Nature Switzerland AG.

关键词： Heart

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Computer-Aided Tumor diagnosis in Automated Breast Ultrasound Using 3d detection Network 23rd

Computer-Aided Tumor Diagnosis in Automated Breast Ultrasoun...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Yu, Junxiong Chen, Chaoyu Yang, Xin Wang, Yi Yan, dan Zhang, Jianxing Ni, dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Guangdong Province Traditional Chinese Medical Hospital GuangZhou China

ISBN: (纸本)9783030597245

Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3d information and coronal plane information with great diagnostic value. However, manually screening and diagnosing tumors from ABUS images is very time-consuming and overlooks of abnormalities may happen. In this study, we propose a novel two-stage 3d detection network for locating suspected lesion areas and further classifying lesions as benign or malignant tumors. Specifically, we propose a 3d detection network rather than frequently-used segmentation network to locate lesions in ABUS images, thus our network can make full use of the spatial context information in ABUS images. A novel similarity loss is designed to effectively distinguish lesions from background. Then a classification network is employed to identify the located lesions as benign or malignant. An IoU-balanced classification loss is adopted to improve the correlation between classification and localization task. The efficacy of our network is verified from a collected dataset of 418 patients with 145 benign tumors and 273 malignant tumors. Experiments show our network attains a sensitivity of 97.66% with 1.23 false positives (FPs), and has an area under the curve(AUC) value of 0.8720. © 2020, Springer Nature Switzerland AG.

关键词： Tumors

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CPM-Net: A 3d Center-Points Matching Network for Pulmonary Nodule detection in CT Scans 23rd

CPM-Net: A 3D Center-Points Matching Network for Pulmonary N...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Song, Tao Chen, Jieneng Luo, Xiangde Huang, Yechong Liu, Xinglong Huang, Ning Chen, Yinan Ye, Zhaoxiang Sheng, Huaqiang Zhang, Shaoting Wang, Guotai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China SenseTime Research Shanghai China Department of Radiology Tianjin Medical University Cancer Institute and Hospital Tianjin China Department of Radiology The First Affiliated Hospital of Shandong First Medical University Jinan China College of Electronics and Information Technology Tongji University Shanghai China

ISBN: (纸本)9783030597245

Automatic and accurate lung nodule detection from Computed Tomography (CT) scans plays a vital role in efficient lung cancer screening. despite the state-of-the-art performance obtained by recent anchor-based detectors using Convolutional Neural Networks (CNNs) for this task, they require pre-determined anchor parameters such as the size, number and aspect ratio of anchors, and have limited robustness when dealing with lung nodules with a massive variety of sizes. To overcome this problem, we propose a 3d center-points matching detection network (CPM-Net) that is anchor-free and automatically predicts the position, size and aspect ratio of nodules without manual design of nodule/anchor parameters. The CPM-Net uses center-points matching strategy to find center-points, and then uses features of these points correspondingly to regress the size of the bounding box of nodule and local offset of the center points. To better capture spatial information and 3d context for the detection, we propose to fuse multi-level spatial coordinate maps with the feature extractor and combine it with 3d squeeze-and-excitation attention modules. To deal with the enormous imbalance between the number of positive and negative samples during center points matching, we propose a hybrid method of adaptive points mining and re-focal loss. Experimental results on LUNA16 dataset showed that our proposed CPM-Net achieved superior performance for lung nodule detection compared with state-of-the-art anchor-based methods. © 2020, Springer Nature Switzerland AG.

关键词： Medical imaging

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Three-dimensional liquid patterning with micromesh structure by 3d printing fabrication 23

Three-dimensional liquid patterning with micromesh structure...

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23rd international conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019

作者： Kim, Suryong Lee, Byungjun Ko, Jihoon Kim, Youngtaek Jeon, Noo Li Seoul National University Seoul Korea Republic of Curiochips Korea Republic of

ISBN: (纸本)9781733419000

In microfluidic system, liquid handling is important to developing high-throughput drug screening, or constructing 3d cell culture platform. However, it was hard to expand this liquid patterning method to 3d system with conventional fabrication technology. Therefore, we suggest a novel concept for 3d patterning of liquid and hydrogel with adapting 3d printed micromesh structure and applied for complex microfluidic channel and 3d co-culture platform of multiple cell types. In addition, we proposed a method of forming a 3d blood vessel network by patterning hydrogel and endothelial cell inside a micromesh, showing the possibility of applying it to tissue engineering application. © 2019 CBMS-0001.

关键词： Microfluidics

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CTC enrichment using a 3d printed device combining immunoaffinity and filtration 23

CTC enrichment using a 3D printed device combining immunoaff...

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23rd international conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019

作者： Chu, Chia-Heng Liu, Ruxiu Ozkaya-Ahmadov, Tevhide Fatih Sarioglu, A. School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781733419000

Microfluidic devices that utilize immunoaffinity-based leukocyte depletion offers high specificity and unbiased circulating tumor cell enrichment. In this paper, we present a 3d printed monolithic device with stacked layers of immunodepletion channels followed by a microfiltration section to deplete leukocytes directly from whole blood and retrieve the cancer cells, respectively. Through device optimization, we were able to deplete up to ~96% of leukocytes in the immunodepletion channels and retrieve ~70% of the cancer cells on the filter. © 2019 CBMS-0001.

关键词： 3d printers

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3d printing of fluorinated polymers to modulate the surface wetting behaviour 23

3D printing of fluorinated polymers to modulate the surface ...

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23rd international conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019

作者： Risch, Patrick Kotz, Frederik Helmer, dorothea Rapp, Bastian E. Laboratory of Process Technology Department of Microsystems Engineering University of Freiburg Freiburg Germany

ISBN: (纸本)9781733419000

In this paper we present 3d printing of superhydrophobic surfaces using a fluorinated perfluoropolyether methacrylate (PFPE-MA) resin in a commercial stereolithography (SL) printer. The 3d printed fluorinated polymer foam is used to generate superhydrophobic surface patterns, which can be easily used to fabricate cell or droplet microarrays. © 2019 CBMS-0001.

关键词： Fluorine containing polymers

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