2,455 research outputs found
Emergence of Object Segmentation in Perturbed Generative Models
We introduce a novel framework to build a model that can learn how to segment
objects from a collection of images without any human annotation. Our method
builds on the observation that the location of object segments can be perturbed
locally relative to a given background without affecting the realism of a
scene. Our approach is to first train a generative model of a layered scene.
The layered representation consists of a background image, a foreground image
and the mask of the foreground. A composite image is then obtained by
overlaying the masked foreground image onto the background. The generative
model is trained in an adversarial fashion against a discriminator, which
forces the generative model to produce realistic composite images. To force the
generator to learn a representation where the foreground layer corresponds to
an object, we perturb the output of the generative model by introducing a
random shift of both the foreground image and mask relative to the background.
Because the generator is unaware of the shift before computing its output, it
must produce layered representations that are realistic for any such random
perturbation. Finally, we learn to segment an image by defining an autoencoder
consisting of an encoder, which we train, and the pre-trained generator as the
decoder, which we freeze. The encoder maps an image to a feature vector, which
is fed as input to the generator to give a composite image matching the
original input image. Because the generator outputs an explicit layered
representation of the scene, the encoder learns to detect and segment objects.
We demonstrate this framework on real images of several object categories.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS
2019), Spotlight presentatio
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.Comment: TPAMI final version, duplicate proposals removed in experiment
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
The health and function of tissue rely on its vasculature network to provide
reliable blood perfusion. Volumetric imaging approaches, such as multiphoton
microscopy, are able to generate detailed 3D images of blood vessels that could
contribute to our understanding of the role of vascular structure in normal
physiology and in disease mechanisms. The segmentation of vessels, a core image
analysis problem, is a bottleneck that has prevented the systematic comparison
of 3D vascular architecture across experimental populations. We explored the
use of convolutional neural networks to segment 3D vessels within volumetric in
vivo images acquired by multiphoton microscopy. We evaluated different network
architectures and machine learning techniques in the context of this
segmentation problem. We show that our optimized convolutional neural network
architecture, which we call DeepVess, yielded a segmentation accuracy that was
better than both the current state-of-the-art and a trained human annotator,
while also being orders of magnitude faster. To explore the effects of aging
and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of
cortical blood vessels in young and old mouse models of Alzheimer's disease and
wild type littermates. We found little difference in the distribution of
capillary diameter or tortuosity between these groups, but did note a decrease
in the number of longer capillary segments () in aged animals as
compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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