627 research outputs found
Generalizable automated pixel-level structural segmentation of medical and biological data
Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These
solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution.
This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D
structural segmentation in a more generalizable manner, yet has enough adaptability to address
a number of specific image modalities, spanning retinal funduscopy, sequential
fluorescein angiography and two-photon microscopy.
The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based
measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D.
To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective
RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-)
pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations.
Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional
exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this
into consideration, we introduce a 5D orientation mapping to capture these orientation properties.
This mapping is incorporated into the local feature map description prior to a learning
machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods.
For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces
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
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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
IterMiUnet: A lightweight architecture for automatic blood vessel segmentation
The automatic segmentation of blood vessels in fundus images can help analyze
the condition of retinal vasculature, which is crucial for identifying various
systemic diseases like hypertension, diabetes, etc. Despite the success of Deep
Learning-based models in this segmentation task, most of them are heavily
parametrized and thus have limited use in practical applications. This paper
proposes IterMiUnet, a new lightweight convolution-based segmentation model
that requires significantly fewer parameters and yet delivers performance
similar to existing models. The model makes use of the excellent segmentation
capabilities of Iternet architecture but overcomes its heavily parametrized
nature by incorporating the encoder-decoder structure of MiUnet model within
it. Thus, the new model reduces parameters without any compromise with the
network's depth, which is necessary to learn abstract hierarchical concepts in
deep models. This lightweight segmentation model speeds up training and
inference time and is potentially helpful in the medical domain where data is
scarce and, therefore, heavily parametrized models tend to overfit. The
proposed model was evaluated on three publicly available datasets: DRIVE,
STARE, and CHASE-DB1. Further cross-training and inter-rater variability
evaluations have also been performed. The proposed model has a lot of potential
to be utilized as a tool for the early diagnosis of many diseases
Curvilinear object segmentation in medical images based on ODoS filter and deep learning network
Automatic segmentation of curvilinear objects in medical images plays an
important role in the diagnosis and evaluation of human diseases, yet it is a
challenging uncertainty in the complex segmentation tasks due to different
issues such as various image appearances, low contrast between curvilinear
objects and their surrounding backgrounds, thin and uneven curvilinear
structures, and improper background illumination conditions. To overcome these
challenges, we present a unique curvilinear structure segmentation framework
based on an oriented derivative of stick (ODoS) filter and a deep learning
network for curvilinear object segmentation in medical images. Currently, a
large number of deep learning models emphasize developing deep architectures
and ignore capturing the structural features of curvilinear objects, which may
lead to unsatisfactory results. Consequently, a new approach that incorporates
an ODoS filter as part of a deep learning network is presented to improve the
spatial attention of curvilinear objects. Specifically, the input image is
transfered into four-channel image constructed by the ODoS filter. In which,
the original image is considered the principal part to describe various image
appearance and complex background illumination conditions, a multi-step
strategy is used to enhance the contrast between curvilinear objects and their
surrounding backgrounds, and a vector field is applied to discriminate thin and
uneven curvilinear structures. Subsequently, a deep learning framework is
employed to extract various structural features for curvilinear object
segmentation in medical images. The performance of the computational model is
validated in experiments conducted on the publicly available DRIVE, STARE and
CHASEDB1 datasets. The experimental results indicate that the presented model
yields surprising results compared with those of some state-of-the-art methods.Comment: 20 pages, 8 figure
- …