Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation

Abstract

Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images. However, there has been little effort to use deep frameworks for volumetric vascular segmentation. We wanted to address this by providing a freely available dataset of 12 annotated two-photon vasculature microscopy stacks. We demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet). Our hybrid 2D-3D architecture produced promising segmentation result. We derived the architectures from Lee et al. who used the ZNN framework initially designed for electron microscope image segmentation. We hope that by sharing our volumetric vasculature datasets, we will inspire other researchers to experiment with vasculature dataset and improve the used network architectures.

arXiv comment23 pages, 10 figures

References in corpus (50)

• Deep Learning in Neural Networks: An Overview
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
• Brain Tumor Segmentation with Deep Neural Networks
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Going Deeper with Convolutions
• OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• Fully Convolutional Networks for Semantic Segmentation
• Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
• Rethinking the Inception Architecture for Computer Vision
• Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning
• Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks
• Some Improvements on Deep Convolutional Neural Network Based Image Classification
• Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
• Recent Advances in Convolutional Neural Networks
• Holistically-Nested Edge Detection
• Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
• Better Mixing via Deep Representations
• Describing Videos by Exploiting Temporal Structure
• Machine learning of hierarchical clustering to segment 2D and 3D images
• Attention to Scale: Scale-aware Semantic Image Segmentation
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
• Efficient piecewise training of deep structured models for semantic segmentation
• Recurrent Convolutional Neural Networks for Scene Parsing
• Analyzing noise in autoencoders and deep networks
• Deep Feature Learning for EEG Recordings
• Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks
• Deeply-Recursive Convolutional Network for Image Super-Resolution
• An exploration of parameter redundancy in deep networks with circulant projections
• Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph
• ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines
• Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
• Deep and Wide Multiscale Recursive Networks for Robust Image Labeling
• A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks
• An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation
• A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling
• Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
• Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
• Combinatorial Energy Learning for Image Segmentation
• Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising
• Vesselness via Multiple Scale Orientation Scores
• Efficient Training of Very Deep Neural Networks for Supervised Hashing
• The Fast Bilateral Solver
• Superpixel Convolutional Networks using Bilateral Inceptions
• Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving
• Active Learning for Delineation of Curvilinear Structures

Cited by in corpus (6)

• A Survey on Deep Learning in Medical Image Analysis
• Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
• A Comprehensive Review of Modern Object Segmentation Approaches
• Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection
• Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models
• Out-of-distribution data supervision towards biomedical semantic segmentation