238 research outputs found
Manifold Learning in Medical Imaging
Manifold learning theory has seen a surge of interest in the modeling of large and extensive datasets in medical imaging since they capture the essence of data in a way that fundamentally outperforms linear methodologies, the purpose of which is to essentially describe things that are flat. This problematic is particularly relevant with medical imaging data, where linear techniques are frequently unsuitable for capturing variations in anatomical structures. In many cases, there is enough structure in the data (CT, MRI, ultrasound) so a lower dimensional object can describe the degrees of freedom, such as in a manifold structure. Still, complex, multivariate distributions tend to demonstrate highly variable structural topologies that are impossible to capture with a single manifold learning algorithm. This chapter will present recent techniques developed in manifold theory for medical imaging analysis, to allow for statistical organ shape modeling, image segmentation and registration from the concept of navigation of manifolds, classification, as well as disease prediction models based on discriminant manifolds. We will present the theoretical basis of these works, with illustrative results on their applications from various organs and pathologies, including neurodegenerative diseases and spinal deformities
Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images
Breast cancer is the most diagnosed cancer and the most predominant cause of
death in women worldwide. Imaging techniques such as the breast cancer
pathology helps in the diagnosis and monitoring of the disease. However
identification of malignant cells can be challenging given the high
heterogeneity in tissue absorbotion from staining agents. In this work, we
present a novel approach for Invasive Ductal Carcinoma (IDC) cells
discrimination in histopathology slides. We propose a model derived from the
Inception architecture, proposing a multi-level batch normalization module
between each convolutional steps. This module was used as a base block for the
feature extraction in a CNN architecture. We used the open IDC dataset in which
we obtained a balanced accuracy of 0.89 and an F1 score of 0.90, thus
surpassing recent state of the art classification algorithms tested on this
public dataset.Comment: 4 pages, 5 figure
Liver lesion segmentation informed by joint liver segmentation
We propose a model for the joint segmentation of the liver and liver lesions
in computed tomography (CT) volumes. We build the model from two fully
convolutional networks, connected in tandem and trained together end-to-end. We
evaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge,
attaining competitive liver and liver lesion detection and segmentation scores
across a wide range of metrics. Unlike other top performing methods, our model
output post-processing is trivial, we do not use data external to the
challenge, and we propose a simple single-stage model that is trained
end-to-end. However, our method nearly matches the top lesion segmentation
performance and achieves the second highest precision for lesion detection
while maintaining high recall.Comment: Late upload of conference version (ISBI
Label noise in segmentation networks : mitigation must deal with bias
Imperfect labels limit the quality of predictions learned by deep neural
networks. This is particularly relevant in medical image segmentation, where
reference annotations are difficult to collect and vary significantly even
across expert annotators. Prior work on mitigating label noise focused on
simple models of mostly uniform noise. In this work, we explore biased and
unbiased errors artificially introduced to brain tumour annotations on MRI
data. We found that supervised and semi-supervised segmentation methods are
robust or fairly robust to unbiased errors but sensitive to biased errors. It
is therefore important to identify the sorts of errors expected in medical
image labels and especially mitigate the biased errors
Reconstruction 3D personnalisée de la colonne vertébrale à partir d'images radiographiques non-calibrées
Les systèmes de reconstruction stéréo-radiographique 3D -- La colonne vertébrale -- La scoliose idiopathique adolescente -- Évolution des systèmes de reconstruction 3D -- Filtres de rehaussement d'images -- Techniques de segmentation -- Les méthodes de calibrage -- Les méthodes de reconstruction 3D -- Problématique, hypothèses, objectifs et méthode générale -- Three-dimensional reconstruction of the scoliotic spine and pelvis from uncalibrated biplanar X-ray images -- A versatile 3D reconstruction system of the spine and pelvis for clinical assessment of spinal deformities -- Simulation experiments -- Clinical validation -- A three-dimensional retrospective analysis of the evolution of spinal instrumentation for the correction of adolescent idiopathic scoliosis -- Auto-calibrage d'un système à rayons-X à partir de primitives de haut niveau -- Segmentation de la colonne vertébrale -- Approche hiérarchique d'auto-calibrage d'un système d'acquisition à rayons-X -- Personalized 3D reconstruction of the scoliotic spine from hybrid statistical and X-ray image-based models -- Validation protocol
Sub-cortical brain structure segmentation using F-CNN's
In this paper we propose a deep learning approach for segmenting sub-cortical
structures of the human brain in Magnetic Resonance (MR) image data. We draw
inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN)
architecture for semantic segmentation of objects in natural images, and adapt
it to our task. Unlike previous CNN-based methods that operate on image
patches, our model is applied on a full blown 2D image, without any alignment
or registration steps at testing time. We further improve segmentation results
by interpreting the CNN output as potentials of a Markov Random Field (MRF),
whose topology corresponds to a volumetric grid. Alpha-expansion is used to
perform approximate inference imposing spatial volumetric homogeneity to the
CNN priors. We compare the performance of the proposed pipeline with a similar
system using Random Forest-based priors, as well as state-of-art segmentation
algorithms, and show promising results on two different brain MRI datasets.Comment: ISBI 2016: International Symposium on Biomedical Imaging, Apr 2016,
Prague, Czech Republi
Inferring Preoperative Reconstructed Spine Models to Volumetric CT Data through High-Order MRFs
In this paper, we introduce a novel approach based on higher order energy functions which have the ability to encode global structural dependencies to infer articulated 3D spine models to CT volume data. A personalized geometrical model is reconstructed from biplanar X-rays before spinal surgery in order to create a spinal column representation which is modeled by a series of intervertebral transformations based on rotation and translation parameters. The shape transformation between the standing and lying poses is then achieved through a Markov Random Field optimization graph, where the unknown variables are the deformations applied to the intervertebral transformations. Singleton and pairwise potentials measure the support from the data and geometrical dependencies between neighboring vertebrae respectively, while higher order cliques are introduced to integrate consistency in regional curves. Optimization of model parameters in a multi-modal context is achieved using efficient linear programming and duality. A qualitative evaluation of the vertebra model alignment obtained from the proposed method gave promising results while the quantitative comparison to expert identification yields an accuracy of 1.8 +/- 0.7mm based on the localization of surgical landmarks
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