3,797 research outputs found
Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss
We present a fully convolutional neural network for segmenting ischemic
stroke lesions in CT perfusion images for the ISLES 2018 challenge. Treatment
of stroke is time sensitive and current standards for lesion identification
require manual segmentation, a time consuming and challenging process.
Automatic segmentation methods present the possibility of accurately
identifying lesions and improving treatment planning. Our model is based on the
PSPNet, a network architecture that makes use of pyramid pooling to provide
global and local contextual information. To learn the varying shapes of the
lesions, we train our network using focal loss, a loss function designed for
the network to focus on learning the more difficult samples. We compare our
model to networks trained using the U-Net and V-Net architectures. Our approach
demonstrates effective performance in lesion segmentation and ranked among the
top performers at the challenge conclusion.Comment: BrainLes 2018 MICCAI worksho
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
MDS-Net: A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation
The irregular geometry and high inter-slice variability in computerized
tomography (CT) scans of the human pancreas make an accurate segmentation of
this crucial organ a challenging task for existing data-driven deep learning
methods. To address this problem, we present a novel model-driven stack-based
fully convolutional network with a bi-directional convolutional long short-term
memory network for pancreas segmentation, termed MDS-Net. The MDS-Net's cost
function includes data approximation term and prior knowledge regularization
term combined with a stack scheme for capturing and fusing the two-dimensional
(2D) and local three-dimensional (3D) context information. Specifically, 3D CT
scans are divided into multiple stacks, and each multi-slice stack is used as a
basic unit for network training and modeling of the local spatial context. To
highlight the importance of single slices in segmentation, the inter-slice
relationships in the stack data are also incorporated in the MDS-Net framework.
For implementing this proposed model-driven method, we create a stack-based
U-Net architecture and successfully derive its back-propagation procedure for
end-to-end training. Furthermore, a bi-directional convolutional long
short-term memory (BiCLSTM) network is utilized to integrate upper and lower
slice information, thereby ensuring the consistency of adjacent CT slices and
intra-stack. Finally, extensive quantitative assessments on the NIH Pancreas-CT
dataset demonstrated higher pancreatic segmentation accuracy and reliability of
MDS-Net compared to other state-of-the-art methods
Towards Automatic 3D Shape Instantiation for Deployed Stent Grafts: 2D Multiple-class and Class-imbalance Marker Segmentation with Equally-weighted Focal U-Net
Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently
navigated by 2D fluoroscopy which is insufficiently informative. Previously, a
semi-automatic 3D shape instantiation method was developed to instantiate the
3D shape of a main, deployed, and fenestrated stent graft from a single
fluoroscopy projection in real-time, which could help 3D FEVAR navigation and
robotic path planning. This proposed semi-automatic method was based on the
Robust Perspective-5-Point (RP5P) method, graft gap interpolation and
semi-automatic multiple-class marker center determination. In this paper, an
automatic 3D shape instantiation could be achieved by automatic multiple-class
marker segmentation and hence automatic multiple-class marker center
determination. Firstly, the markers were designed into five different shapes.
Then, Equally-weighted Focal U-Net was proposed to segment the fluoroscopy
projections of customized markers into five classes and hence to determine the
marker centers. The proposed Equally-weighted Focal U-Net utilized U-Net as the
network architecture, equally-weighted loss function for initial marker
segmentation, and then equally-weighted focal loss function for improving the
initial marker segmentation. This proposed network outperformed traditional
Weighted U-Net on the class-imbalance segmentation in this paper with reducing
one hyper-parameter - the weight. An overall mean Intersection over Union
(mIoU) of 0.6943 was achieved on 78 testing images, where 81.01% markers were
segmented with a center position error <1.6mm. Comparable accuracy of 3D shape
instantiation was also achieved and stated. The data, trained models and
TensorFlow codes are available on-line.Comment: 7 pages, 8 figures, 2 table
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
Deep convolutional neural networks have achieved remarkable progress on a
variety of medical image computing tasks. A common problem when applying
supervised deep learning methods to medical images is the lack of labeled data,
which is very expensive and time-consuming to be collected. In this paper, we
present a novel semi-supervised method for medical image segmentation, where
the network is optimized by the weighted combination of a common supervised
loss for labeled inputs only and a regularization loss for both labeled and
unlabeled data. To utilize the unlabeled data, our method encourages the
consistent predictions of the network-in-training for the same input under
different regularizations. Aiming for the semi-supervised segmentation problem,
we enhance the effect of regularization for pixel-level predictions by
introducing a transformation, including rotation and flipping, consistent
scheme in our self-ensembling model. With the aim of semi-supervised
segmentation tasks, we introduce a transformation consistent strategy in our
self-ensembling model to enhance the regularization effect for pixel-level
predictions. We have extensively validated the proposed semi-supervised method
on three typical yet challenging medical image segmentation tasks: (i) skin
lesion segmentation from dermoscopy images on International Skin Imaging
Collaboration (ISIC) 2017 dataset, (ii) optic disc segmentation from fundus
images on Retinal Fundus Glaucoma Challenge (REFUGE) dataset, and (iii) liver
segmentation from volumetric CT scans on Liver Tumor Segmentation Challenge
(LiTS) dataset. Compared to the state-of-the-arts, our proposed method shows
superior segmentation performance on challenging 2D/3D medical images,
demonstrating the effectiveness of our semi-supervised method for medical image
segmentation.Comment: Accept at IEEE Transactions on Neural Networks and Learning System
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial
for making treatment decisions, but can be challenging even for experienced
radiologists. The diagnostic procedure is based on the detection and
recognition of the different ILD pathologies in thoracic CT scans, yet their
manifestation often appears similar. In this study, we propose the use of a
deep purely convolutional neural network for the semantic segmentation of ILD
patterns, as the basic component of a computer aided diagnosis (CAD) system for
ILDs. The proposed CNN, which consists of convolutional layers with dilated
filters, takes as input a lung CT image of arbitrary size and outputs the
corresponding label map. We trained and tested the network on a dataset of 172
sparsely annotated CT scans, within a cross-validation scheme. The training was
performed in an end-to-end and semi-supervised fashion, utilizing both labeled
and non-labeled image regions. The experimental results show significant
performance improvement with respect to the state of the art
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications. Methods
based on convolutional neural networks attain state-of-the-art accuracy;
however, they typically rely on supervised training with large labeled
datasets. Labeling medical images requires significant expertise and time, and
typical hand-tuned approaches for data augmentation fail to capture the complex
variations in such images.
We present an automated data augmentation method for synthesizing labeled
medical images. We demonstrate our method on the task of segmenting magnetic
resonance imaging (MRI) brain scans. Our method requires only a single
segmented scan, and leverages other unlabeled scans in a semi-supervised
approach. We learn a model of transformations from the images, and use the
model along with the labeled example to synthesize additional labeled examples.
Each transformation is comprised of a spatial deformation field and an
intensity change, enabling the synthesis of complex effects such as variations
in anatomy and image acquisition procedures. We show that training a supervised
segmenter with these new examples provides significant improvements over
state-of-the-art methods for one-shot biomedical image segmentation. Our code
is available at https://github.com/xamyzhao/brainstorm.Comment: 9 pages, CVPR 201
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
Semantic segmentation is essentially important to biomedical image analysis.
Many recent works mainly focus on integrating the Fully Convolutional Network
(FCN) architecture with sophisticated convolution implementation and deep
supervision. In this paper, we propose to decompose the single segmentation
task into three subsequent sub-tasks, including (1) pixel-wise image
segmentation, (2) prediction of the class labels of the objects within the
image, and (3) classification of the scene the image belonging to. While these
three sub-tasks are trained to optimize their individual loss functions of
different perceptual levels, we propose to let them interact by the task-task
context ensemble. Moreover, we propose a novel sync-regularization to penalize
the deviation between the outputs of the pixel-wise segmentation and the class
prediction tasks. These effective regularizations help FCN utilize context
information comprehensively and attain accurate semantic segmentation, even
though the number of the images for training may be limited in many biomedical
applications. We have successfully applied our framework to three diverse 2D/3D
medical image datasets, including Robotic Scene Segmentation Challenge 18
(ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus
Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all
three challenges.Comment: IEEE Transactions on Medical Imagin
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
Finding and identifying scatteredly-distributed, small, and critically
important objects in 3D oncology images is very challenging. We focus on the
detection and segmentation of oncology-significant (or suspicious cancer
metastasized) lymph nodes (OSLNs), which has not been studied before as a
computational task. Determining and delineating the spread of OSLNs is
essential in defining the corresponding resection/irradiating regions for the
downstream workflows of surgical resection and radiotherapy of various cancers.
For patients who are treated with radiotherapy, this task is performed by
experienced radiation oncologists that involves high-level reasoning on whether
LNs are metastasized, which is subject to high inter-observer variations. In
this work, we propose a divide-and-conquer decision stratification approach
that divides OSLNs into tumor-proximal and tumor-distal categories. This is
motivated by the observation that each category has its own different
underlying distributions in appearance, size and other characteristics. Two
separate detection-by-segmentation networks are trained per category and fused.
To further reduce false positives (FP), we present a novel global-local network
(GLNet) that combines high-level lesion characteristics with features learned
from localized 3D image patches. Our method is evaluated on a dataset of 141
esophageal cancer patients with PET and CT modalities (the largest to-date).
Our results significantly improve the recall from to at FPs
per patient as compared to previous state-of-the-art methods. The highest
achieved OSLN recall of is clinically relevant and valuable.Comment: 14 pages, 4 Figure
Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT
We consider the problem of localizing and segmenting individual teeth inside
3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we
approach this task with a coarse-to-fine framework, where the whole volume is
first analyzed as a 33-class semantic segmentation (adults have up to 32 teeth)
in coarse resolution, followed by binary semantic segmentation of the cropped
region of interest in original resolution. To improve the performance of the
challenging 33-class segmentation, we first train the Coarse step model on a
large weakly labeled dataset, then fine-tune it on a smaller precisely labeled
dataset. The Fine step model is trained with precise labels only. Experiments
using our in-house dataset show significant improvement for both
weakly-supervised pretraining and for the addition of the Fine step.
Empirically, this framework yields precise teeth masks with low localization
errors sufficient for many real-world applications
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