594 research outputs found
Discrete Visual Perception
International audienceComputational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the interest of such representations, discuss their strength and limitations and present their application to address a variety of problems in computer vision and biomedical image analysis
Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
Segmentation of the heart in cardiac cine MR is clinically used to quantify
cardiac function. We propose a fully automatic method for segmentation and
disease classification using cardiac cine MR images. A convolutional neural
network (CNN) was designed to simultaneously segment the left ventricle (LV),
right ventricle (RV) and myocardium in end-diastole (ED) and end-systole (ES)
images. Features derived from the obtained segmentations were used in a Random
Forest classifier to label patients as suffering from dilated cardiomyopathy,
hypertrophic cardiomyopathy, heart failure following myocardial infarction,
right ventricular abnormality, or no cardiac disease. The method was developed
and evaluated using a balanced dataset containing images of 100 patients, which
was provided in the MICCAI 2017 automated cardiac diagnosis challenge (ACDC).
The segmentation and classification pipeline were evaluated in a four-fold
stratified cross-validation. Average Dice scores between reference and
automatically obtained segmentations were 0.94, 0.88 and 0.87 for the LV, RV
and myocardium. The classifier assigned 91% of patients to the correct disease
category. Segmentation and disease classification took 5 s per patient. The
results of our study suggest that image-based diagnosis using cine MR cardiac
scans can be performed automatically with high accuracy.Comment: Accepted in STACOM Automated Cardiac Diagnosis Challenge 201
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location
Background and Purpose: Pediatric low-grade glioma (pLGG) is the most common
type of brain tumor in children, and identification of molecular markers for
pLGG is crucial for successful treatment planning. Convolutional Neural Network
(CNN) models for pLGG subtype identification rely on tumor segmentation. We
hypothesize tumor segmentations are suboptimal and thus, we propose to augment
the CNN models using tumor location probability in MRI data.
Materials and Methods: Our REB-approved retrospective study included MRI
Fluid-Attenuated Inversion Recovery (FLAIR) sequences of 143 BRAF fused and 71
BRAF V600E mutated tumors. Tumor segmentations (regions of interest (ROIs))
were provided by a pediatric neuroradiology fellow and verified by a senior
pediatric neuroradiologist. In each experiment, we randomly split the data into
development and test with an 80/20 ratio. We combined the 3D binary ROI masks
for each class in the development dataset to derive the probability density
functions (PDF) of tumor location, and developed three pipelines:
location-based, CNN-based, and hybrid.
Results: We repeated the experiment with different model initializations and
data splits 100 times and calculated the Area Under Receiver Operating
Characteristic Curve (AUC). The location-based classifier achieved an AUC of
77.90, 95% confidence interval (CI) (76.76, 79.03). CNN-based classifiers
achieved AUC of 86.11, CI (84.96, 87.25), while the tumor-location-guided CNNs
outperformed the formers with an average AUC of 88.64 CI (87.57, 89.72), which
was statistically significant (Student's t-test p-value 0.0018).
Conclusion: We achieved statistically significant improvements by
incorporating tumor location into the CNN models. Our results suggest that
manually segmented ROIs may not be optimal.Comment: arXiv admin note: text overlap with arXiv:2207.1477
"Mental Rotation" by Optimizing Transforming Distance
The human visual system is able to recognize objects despite transformations
that can drastically alter their appearance. To this end, much effort has been
devoted to the invariance properties of recognition systems. Invariance can be
engineered (e.g. convolutional nets), or learned from data explicitly (e.g.
temporal coherence) or implicitly (e.g. by data augmentation). One idea that
has not, to date, been explored is the integration of latent variables which
permit a search over a learned space of transformations. Motivated by evidence
that people mentally simulate transformations in space while comparing
examples, so-called "mental rotation", we propose a transforming distance.
Here, a trained relational model actively transforms pairs of examples so that
they are maximally similar in some feature space yet respect the learned
transformational constraints. We apply our method to nearest-neighbour problems
on the Toronto Face Database and NORB
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
- …