2,500 research outputs found
Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network
Recent advances in deep learning have improved the segmentation accuracy of
subcortical brain structures, which would be useful in neuroimaging studies of
many neurological disorders. However, most of the previous deep learning work
does not investigate the specific difficulties that exist in segmenting
extremely small but important brain regions such as the amygdala and its
subregions. To tackle this challenging task, a novel 3D Bayesian fully
convolutional neural network was developed to apply a dilated dualpathway
approach that retains fine details and utilizes both local and more global
contextual information to automatically segment the amygdala and its subregions
at high precision. The proposed method provides insights on network design and
sampling strategy that target segmentations of small 3D structures. In
particular, this study confirms that a large context, enabled by a large field
of view, is beneficial for segmenting small objects; furthermore, precise
contextual information enabled by dilated convolutions allows for better
boundary localization, which is critical for examining the morphology of the
structure. In addition, it is demonstrated that the uncertainty information
estimated from our network may be leveraged to identify atypicality in data.
Our method was compared with two state-of-the-art deep learning models and a
traditional multi-atlas approach, and exhibited excellent performance as
measured both by Dice overlap as well as average symmetric surface distance. To
the best of our knowledge, this work is the first deep learning-based approach
that targets the subregions of the amygdala
Deep analytics of atomically-resolved images: manifest and latent features
Recent advances in scanning transmission electron and scanning tunneling
microscopies allow researchers to measure materials structural and electronic
properties, such as atomic displacements and charge density modulations, at an
Angstrom scale in real space. At the same time, the ability to quickly acquire
large, high-resolution datasets has created a challenge for rapid physics-based
analysis of images that typically contain several hundreds to several thousand
atomic units. Here we demonstrate a universal deep-learning based framework for
locating and characterizing atomic species in the lattice, which can be applied
to different types of atomically resolved measurements on different materials.
Specifically, by inspecting and categorizing features in the output layer of a
convolutional neural network, we are able to detect structural and electronic
'anomalies' associated with the presence of point defects in a tungsten
disulfide monolayer, non-uniformity of the charge density distribution around
specific lattice sites on the surface of strongly correlated oxides, and
transition between different structural states of buckybowl molecules. We
further extended our method towards tracking, from one image frame to another,
minute distortions in the geometric shape of individual Si dumbbells in a
3-dimensional Si sample, which are associated with a motion of lattice defects
and impurities. Due the applicability of our framework to both scanning
tunneling microscopy and scanning transmission electron microscopy
measurements, it can provide a fast and straightforward way towards creating a
unified database of defect-property relationships from experimental data for
each material
Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks
Precise segmentation of bladder walls and tumor regions is an essential step
towards non-invasive identification of tumor stage and grade, which is critical
for treatment decision and prognosis of patients with bladder cancer (BC).
However, the automatic delineation of bladder walls and tumor in magnetic
resonance images (MRI) is a challenging task, due to important bladder shape
variations, strong intensity inhomogeneity in urine and very high variability
across population, particularly on tumors appearance. To tackle these issues,
we propose to use a deep fully convolutional neural network. The proposed
network includes dilated convolutions to increase the receptive field without
incurring extra cost nor degrading its performance. Furthermore, we introduce
progressive dilations in each convolutional block, thereby enabling extensive
receptive fields without the need for large dilation rates. The proposed
network is evaluated on 3.0T T2-weighted MRI scans from 60 pathologically
confirmed patients with BC. Experiments shows the proposed model to achieve
high accuracy, with a mean Dice similarity coefficient of 0.98, 0.84 and 0.69
for inner wall, outer wall and tumor region, respectively. These results
represent a very good agreement with reference contours and an increase in
performance compared to existing methods. In addition, inference times are less
than a second for a whole 3D volume, which is between 2-3 orders of magnitude
faster than related state-of-the-art methods for this application. We showed
that a CNN can yield precise segmentation of bladder walls and tumors in
bladder cancer patients on MRI. The whole segmentation process is
fully-automatic and yields results in very good agreement with the reference
standard, demonstrating the viability of deep learning models for the automatic
multi-region segmentation of bladder cancer MRI images.Comment: Published at the journal of Medical Physic
Densely Dilated Spatial Pooling Convolutional Network using benign loss functions for imbalanced volumetric prostate segmentation
The high incidence rate of prostate disease poses a requirement in early
detection for diagnosis. As one of the main imaging methods used for prostate
cancer detection, Magnetic Resonance Imaging (MRI) has wide range of appearance
and imbalance problems, making automated prostate segmentation fundamental but
challenging. Here we propose a novel Densely Dilated Spatial Pooling
Convolutional Network (DDSP ConNet) in encoder-decoder structure. It employs
dense structure to combine dilated convolution and global pooling, thus
supplies coarse segmentation results from encoder and decoder subnet and
preserves more contextual information. To obtain richer hierarchical feature
maps, residual long connection is furtherly adopted to fuse contexture
features. Meanwhile, we adopt DSC loss and Jaccard loss functions to train our
DDSP ConNet. We surprisingly found and proved that, in contrast to re-weighted
cross entropy, DSC loss and Jaccard loss have a lot of benign properties in
theory, including symmetry, continuity and differentiability about the
parameters of network. Extensive experiments on the MICCAI PROMISE12 challenge
dataset have been done to corroborate the effectiveness of our DDSP ConNet with
DSC loss and Jaccard loss. Totally, our method achieves a score of 85.78 in the
test dataset, outperforming most of other competitors.Comment: 14pages, 5 figures, anonymous review in IJACAI201
Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy
This paper attempts to provide the reader a place to begin studying the
application of computer vision and machine learning to gastrointestinal (GI)
endoscopy. They have been classified into 18 categories. It should be be noted
by the reader that this is a review from pre-deep learning era. A lot of deep
learning based applications have not been covered in this thesis
Instance Segmentation of Biological Images Using Harmonic Embeddings
We present a new instance segmentation approach tailored to biological
images, where instances may correspond to individual cells, organisms or plant
parts. Unlike instance segmentation for user photographs or road scenes, in
biological data object instances may be particularly densely packed, the
appearance variation may be particularly low, the processing power may be
restricted, while, on the other hand, the variability of sizes of individual
instances may be limited. The proposed approach successfully addresses these
peculiarities.
Our approach describes each object instance using an expectation of a limited
number of sine waves with frequencies and phases adjusted to particular object
sizes and densities. At train time, a fully-convolutional network is learned to
predict the object embeddings at each pixel using a simple pixelwise regression
loss, while at test time the instances are recovered using clustering in the
embedding space. In the experiments, we show that our approach outperforms
previous embedding-based instance segmentation approaches on a number of
biological datasets, achieving state-of-the-art on a popular CVPPP benchmark.
This excellent performance is combined with computational efficiency that is
needed for deployment to domain specialists.
The source code of the approach is available at
https://github.com/kulikovv/harmonicComment: Accepted as oral to CVPR 202
DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation through SwaftNet for Surrounding Sensing
Semantically interpreting the traffic scene is crucial for autonomous
transportation and robotics systems. However, state-of-the-art semantic
segmentation pipelines are dominantly designed to work with pinhole cameras and
train with narrow Field-of-View (FoV) images. In this sense, the perception
capacity is severely limited to offer higher-level confidence for upstream
navigation tasks. In this paper, we propose a network adaptation framework to
achieve Panoramic Annular Semantic Segmentation (PASS), which allows to re-use
conventional pinhole-view image datasets, enabling modern segmentation networks
to comfortably adapt to panoramic images. Specifically, we adapt our proposed
SwaftNet to enhance the sensitivity to details by implementing attention-based
lateral connections between the detail-critical encoder layers and the
context-critical decoder layers. We benchmark the performance of efficient
segmenters on panoramic segmentation with our extended PASS dataset,
demonstrating that the proposed real-time SwaftNet outperforms state-of-the-art
efficient networks. Furthermore, we assess real-world performance when
deploying the Detail-Sensitive PASS (DS-PASS) system on a mobile robot and an
instrumented vehicle, as well as the benefit of panoramic semantics for visual
odometry, showing the robustness and potential to support diverse navigational
applications.Comment: 8 pages, 10 figure
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
Atom-probe tomography (APT) facilitates nano- and atomic-scale
characterization and analysis of microstructural features. Specifically, APT is
well suited to study the interfacial properties of granular or heterophase
systems. Traditionally, the identification of the interface between, for
precipitate and matrix phases, in APT data has been obtained either by
extracting iso-concentration surfaces based on a user-supplied concentration
value or by manually perturbing the concentration value until the
iso-concentration surface qualitatively matches the interface. These approaches
are subjective, not scalable, and may lead to inconsistencies due to local
composition inhomogeneities.
We propose a digital image segmentation approach based on deep neural
networks that transfer learned knowledge from natural images to automatically
segment the data obtained from APT into different phases. This approach not
only provides an efficient way to segment the data and extract interfacial
properties but does so without the need for expensive interface labeling for
training the segmentation model.
We consider here a system with a precipitate phase in a matrix and with three
different interface modalities---layered, isolated, and interconnected---that
are obtained for different relative geometries of the precipitate phase. We
demonstrate the accuracy of our segmentation approach through qualitative
visualization of the interfaces, as well as through quantitative comparisons
with proximity histograms obtained by using more traditional approaches.Comment: 23 pages, 6 figure
Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey
Osteoarthritis (OA) is one of the major health issues among the elderly
population. MRI is the most popular technology to observe and evaluate the
progress of OA course. However, the extreme labor cost of MRI analysis makes
the process inefficient and expensive. Also, due to human error and subjective
nature, the inter- and intra-observer variability is rather high.
Computer-aided knee MRI segmentation is currently an active research field
because it can alleviate doctors and radiologists from the time consuming and
tedious job, and improve the diagnosis performance which has immense potential
for both clinic and scientific research. In the past decades, researchers have
investigated automatic/semi-automatic knee MRI segmentation methods
extensively. However, to the best of our knowledge, there is no comprehensive
survey paper in this field yet. In this survey paper, we classify the existing
methods by their principles and discuss the current research status and point
out the future research trend in-depth.Comment: 10 pages, 6 table
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images
This paper addresses the task of nuclei segmentation in high-resolution
histopathological images. We propose an auto- matic end-to-end deep neural
network algorithm for segmenta- tion of individual nuclei. A nucleus-boundary
model is introduced to predict nuclei and their boundaries simultaneously using
a fully convolutional neural network. Given a color normalized image, the model
directly outputs an estimated nuclei map and a boundary map. A simple, fast and
parameter-free post-processing procedure is performed on the estimated nuclei
map to produce the final segmented nuclei. An overlapped patch extraction and
assembling method is also designed for seamless prediction of nuclei in large
whole-slide images. We also show the effectiveness of data augmentation methods
for nuclei segmentation task. Our experiments showed our method outperforms
prior state-of-the- art methods. Moreover, it is efficient that one 1000X1000
image can be segmented in less than 5 seconds. This makes it possible to
precisely segment the whole-slide image in acceptable timeComment: 13 pages. 12 figure
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