25,831 research outputs found
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Colorectal adenocarcinoma originating in intestinal glandular structures is
the most common form of colon cancer. In clinical practice, the morphology of
intestinal glands, including architectural appearance and glandular formation,
is used by pathologists to inform prognosis and plan the treatment of
individual patients. However, achieving good inter-observer as well as
intra-observer reproducibility of cancer grading is still a major challenge in
modern pathology. An automated approach which quantifies the morphology of
glands is a solution to the problem. This paper provides an overview to the
Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at
MICCAI'2015. Details of the challenge, including organization, dataset and
evaluation criteria, are presented, along with the method descriptions and
evaluation results from the top performing methods
Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries
According to official statistics, cancer is considered as the second leading
cause of human fatalities. Among different types of cancer, brain tumor is seen
as one of the deadliest forms due to its aggressive nature, heterogeneous
characteristics, and low relative survival rate. Determining the type of brain
tumor has significant impact on the treatment choice and patient's survival.
Human-centered diagnosis is typically error-prone and unreliable resulting in a
recent surge of interest to automatize this process using convolutional neural
networks (CNNs). CNNs, however, fail to fully utilize spatial relations, which
is particularly harmful for tumor classification, as the relation between the
tumor and its surrounding tissue is a critical indicator of the tumor's type.
In our recent work, we have incorporated newly developed CapsNets to overcome
this shortcoming. CapsNets are, however, highly sensitive to the miscellaneous
image background. The paper addresses this gap. The main contribution is to
equip CapsNet with access to the tumor surrounding tissues, without distracting
it from the main target. A modified CapsNet architecture is, therefore,
proposed for brain tumor classification, which takes the tumor coarse
boundaries as extra inputs within its pipeline to increase the CapsNet's focus.
The proposed approach noticeably outperforms its counterparts
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics
Pixel-accurate tracking of objects is a key element in many computer vision
applications, often solved by iterated individual object tracking or instance
segmentation followed by object matching. Here we introduce
cross-classification clustering (3C), a technique that simultaneously tracks
complex, interrelated objects in an image stack. The key idea in
cross-classification is to efficiently turn a clustering problem into a
classification problem by running a logarithmic number of independent
classifications per image, letting the cross-labeling of these classifications
uniquely classify each pixel to the object labels. We apply the 3C mechanism to
achieve state-of-the-art accuracy in connectomics -- the nanoscale mapping of
neural tissue from electron microscopy volumes. Our reconstruction system
increases scalability by an order of magnitude over existing single-object
tracking methods (such as flood-filling networks). This scalability is
important for the deployment of connectomics pipelines, since currently the
best performing techniques require computing infrastructures that are beyond
the reach of most laboratories. Our algorithm may offer benefits in other
domains that require pixel-accurate tracking of multiple objects, such as
segmentation of videos and medical imagery.Comment: 11 figure
Deep Part Induction from Articulated Object Pairs
Object functionality is often expressed through part articulation -- as when
the two rigid parts of a scissor pivot against each other to perform the
cutting function. Such articulations are often similar across objects within
the same functional category. In this paper, we explore how the observation of
different articulation states provides evidence for part structure and motion
of 3D objects. Our method takes as input a pair of unsegmented shapes
representing two different articulation states of two functionally related
objects, and induces their common parts along with their underlying rigid
motion. This is a challenging setting, as we assume no prior shape structure,
no prior shape category information, no consistent shape orientation, the
articulation states may belong to objects of different geometry, plus we allow
inputs to be noisy and partial scans, or point clouds lifted from RGB images.
Our method learns a neural network architecture with three modules that
respectively propose correspondences, estimate 3D deformation flows, and
perform segmentation. To achieve optimal performance, our architecture
alternates between correspondence, deformation flow, and segmentation
prediction iteratively in an ICP-like fashion. Our results demonstrate that our
method significantly outperforms state-of-the-art techniques in the task of
discovering articulated parts of objects. In addition, our part induction is
object-class agnostic and successfully generalizes to new and unseen objects
Gland Instance Segmentation by Deep Multichannel Neural Networks
In this paper, we propose a new image instance segmentation method that
segments individual glands (instances) in colon histology images. This is a
task called instance segmentation that has recently become increasingly
important. The problem is challenging since not only do the glands need to be
segmented from the complex background, they are also required to be
individually identified. Here we leverage the idea of image-to-image prediction
in recent deep learning by building a framework that automatically exploits and
fuses complex multichannel information, regional, location and boundary
patterns in gland histology images. Our proposed system, deep multichannel
framework, alleviates heavy feature design due to the use of convolutional
neural networks and is able to meet multifarious requirement by altering
channels. Compared to methods reported in the 2015 MICCAI Gland Segmentation
Challenge and other currently prevalent methods of instance segmentation, we
observe state-of-the-art results based on a number of evaluation metrics
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving
Deep learning is a fast-growing machine learning approach to perceive and
understand large amounts of data. In this paper, general information about the
deep learning approach which is attracted much attention in the field of
machine learning is given in recent years and an application about semantic
image segmentation is carried out in order to help autonomous driving of
autonomous vehicles. This application is implemented with Fully Convolutional
Network (FCN) architectures obtained by modifying the Convolutional Neural
Network (CNN) architectures based on deep learning. Experimental studies for
the application are utilized 4 different FCN architectures named
FCN-AlexNet,FCN-8s, FCN-16s and FCN-32s. For the experimental studies, FCNs are
first trained separately and validation accuracies of these trained network
models on the used dataset is compared. In addition, image segmentation
inferences are visualized to take account of how precisely FCN architectures
can segment objects.Comment: A chapter for Springer Book: Handbook of Deep Learning Applications,
2018,[ Pijush Samui, Editor]. (be published
A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards
We present new methods for apple detection and counting based on recent deep
learning approaches and compare them with state-of-the-art results based on
classical methods. Our goal is to quantify performance improvements by neural
network-based methods compared to methods based on classical approaches.
Additionally, we introduce a complete system for counting apples in an entire
row. This task is challenging as it requires tracking fruits in images from
both sides of the row. We evaluate the performances of three fruit detection
methods and two fruit counting methods on six datasets. Results indicate that
the classical detection approach still outperforms the deep learning based
methods in the majority of the datasets. For fruit counting though, the deep
learning based approach performs better for all of the datasets. Combining the
classical detection method together with the neural network based counting
approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.Comment: 28 page
X-View: Graph-Based Semantic Multi-View Localization
Global registration of multi-view robot data is a challenging task.
Appearance-based global localization approaches often fail under drastic
view-point changes, as representations have limited view-point invariance. This
work is based on the idea that human-made environments contain rich semantics
which can be used to disambiguate global localization. Here, we present X-View,
a Multi-View Semantic Global Localization system. X-View leverages semantic
graph descriptor matching for global localization, enabling localization under
drastically different view-points. While the approach is general in terms of
the semantic input data, we present and evaluate an implementation on visual
data. We demonstrate the system in experiments on the publicly available
SYNTHIA dataset, on a realistic urban dataset recorded with a simulator, and on
real-world StreetView data. Our findings show that X-View is able to globally
localize aerial-to-ground, and ground-to-ground robot data of drastically
different view-points. Our approach achieves an accuracy of up to 85 % on
global localizations in the multi-view case, while the benchmarked baseline
appearance-based methods reach up to 75 %
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
High-resolution microscopy images of tissue specimens provide detailed
information about the morphology of normal and diseased tissue. Image analysis
of tissue morphology can help cancer researchers develop a better understanding
of cancer biology. Segmentation of nuclei and classification of tissue images
are two common tasks in tissue image analysis. Development of accurate and
efficient algorithms for these tasks is a challenging problem because of the
complexity of tissue morphology and tumor heterogeneity. In this paper we
present two computer algorithms; one designed for segmentation of nuclei and
the other for classification of whole slide tissue images. The segmentation
algorithm implements a multiscale deep residual aggregation network to
accurately segment nuclear material and then separate clumped nuclei into
individual nuclei. The classification algorithm initially carries out
patch-level classification via a deep learning method, then patch-level
statistical and morphological features are used as input to a random forest
regression model for whole slide image classification. The segmentation and
classification algorithms were evaluated in the MICCAI 2017 Digital Pathology
challenge. The segmentation algorithm achieved an accuracy score of 0.78. The
classification algorithm achieved an accuracy score of 0.81
Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation
Convex Shapes (CS) are common priors for optic disc and cup segmentation in
eye fundus images. It is important to design proper techniques to represent
convex shapes. So far, it is still a problem to guarantee that the output
objects from a Deep Neural Convolution Networks (DCNN) are convex shapes. In
this work, we propose a technique which can be easily integrated into the
commonly used DCNNs for image segmentation and guarantee that outputs are
convex shapes. This method is flexible and it can handle multiple objects and
allow some of the objects to be convex. Our method is based on the dual
representation of the sigmoid activation function in DCNNs. In the dual space,
the convex shape prior can be guaranteed by a simple quadratic constraint on a
binary representation of the shapes. Moreover, our method can also integrate
spatial regularization and some other shape prior using a soft thresholding
dynamics (STD) method. The regularization can make the boundary curves of the
segmentation objects to be simultaneously smooth and convex. We design a very
stable active set projection algorithm to numerically solve our model. This
algorithm can form a new plug-and-play DCNN layer called CS-STD whose outputs
must be a nearly binary segmentation of convex objects. In the CS-STD block,
the convexity information can be propagated to guide the DCNN in both forward
and backward propagation during training and prediction process. As an
application example, we apply the convexity prior layer to the retinal fundus
images segmentation by taking the popular DeepLabV3+ as a backbone network.
Experimental results on several public datasets show that our method is
efficient and outperforms the classical DCNN segmentation methods
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