162 research outputs found
Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
The automatic segmentation of human knee cartilage from 3D MR images is a
useful yet challenging task due to the thin sheet structure of the cartilage
with diffuse boundaries and inhomogeneous intensities. In this paper, we
present an iterative multi-class learning method to segment the femoral, tibial
and patellar cartilage simultaneously, which effectively exploits the spatial
contextual constraints between bone and cartilage, and also between different
cartilages. First, based on the fact that the cartilage grows in only certain
area of the corresponding bone surface, we extract the distance features of not
only to the surface of the bone, but more informatively, to the densely
registered anatomical landmarks on the bone surface. Second, we introduce a set
of iterative discriminative classifiers that at each iteration, probability
comparison features are constructed from the class confidence maps derived by
previously learned classifiers. These features automatically embed the semantic
context information between different cartilages of interest. Validated on a
total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the
proposed approach demonstrates high robustness and accuracy of segmentation in
comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron
microscopic (EM) brain images are critical for the field of connectomics. An
important computation for reconstruction is the detection of neuronal
boundaries. Images acquired by serial section EM, a leading 3D EM technique,
are highly anisotropic, with inferior quality along the third dimension. For
such images, the 2D max-pooling convolutional network has set the standard for
performance at boundary detection. Here we achieve a substantial gain in
accuracy through three innovations. Following the trend towards deeper networks
for object recognition, we use a much deeper network than previously employed
for boundary detection. Second, we incorporate 3D as well as 2D filters, to
enable computations that use 3D context. Finally, we adopt a recursively
trained architecture in which a first network generates a preliminary boundary
map that is provided as input along with the original image to a second network
that generates a final boundary map. Backpropagation training is accelerated by
ZNN, a new implementation of 3D convolutional networks that uses multicore CPU
parallelism for speed. Our hybrid 2D-3D architecture could be more generally
applicable to other types of anisotropic 3D images, including video, and our
recursive framework for any image labeling problem
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
Semantic Segmentation using deep convolutional neural network pose more
complex challenge for any GPU intensive task. As it has to compute million of
parameters, it results to huge memory consumption. Moreover, extracting finer
features and conducting supervised training tends to increase the complexity.
With the introduction of Fully Convolutional Neural Network, which uses finer
strides and utilizes deconvolutional layers for upsampling, it has been a go to
for any image segmentation task. In this paper, we propose two segmentation
architecture which not only needs one-third the parameters to compute but also
gives better accuracy than the similar architectures. The model weights were
transferred from the popular neural net like VGG19 and VGG16 which were trained
on Imagenet classification data-set. Then we transform all the fully connected
layers to convolutional layers and use dilated convolution for decreasing the
parameters. Lastly, we add finer strides and attach four skip architectures
which are element-wise summed with the deconvolutional layers in steps. We
train and test on different sparse and fine data-sets like Pascal VOC2012,
Pascal-Context and NYUDv2 and show how better our model performs in this tasks.
On the other hand our model has a faster inference time and consumes less
memory for training and testing on NVIDIA Pascal GPUs, making it more efficient
and less memory consuming architecture for pixel-wise segmentation.Comment: 8 page
Recurrent Human Pose Estimation
We propose a novel ConvNet model for predicting 2D human body poses in an
image. The model regresses a heatmap representation for each body keypoint, and
is able to learn and represent both the part appearances and the context of the
part configuration. We make the following three contributions: (i) an
architecture combining a feed forward module with a recurrent module, where the
recurrent module can be run iteratively to improve the performance, (ii) the
model can be trained end-to-end and from scratch, with auxiliary losses
incorporated to improve performance, (iii) we investigate whether keypoint
visibility can also be predicted. The model is evaluated on two benchmark
datasets. The result is a simple architecture that achieves performance on par
with the state of the art, but without the complexity of a graphical model
stage (or layers).Comment: FG 2017, More Info and Demo:
http://www.robots.ox.ac.uk/~vgg/software/keypoint_detection
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