261 research outputs found
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Quantitative analysis of brain MRI at the age of 6 months is difficult
because of the limited contrast between white matter and gray matter. In this
study, we use a dilated triplanar convolutional neural network in combination
with a non-dilated 3D convolutional neural network for the segmentation of
white matter, gray matter and cerebrospinal fluid in infant brain MR images, as
provided by the MICCAI grand challenge on 6-month infant brain MRI
segmentation.Comment: MICCAI grand challenge on 6-month infant brain MRI segmentatio
Exploring the similarity of medical imaging classification problems
Supervised learning is ubiquitous in medical image analysis. In this paper we
consider the problem of meta-learning -- predicting which methods will perform
well in an unseen classification problem, given previous experience with other
classification problems. We investigate the first step of such an approach: how
to quantify the similarity of different classification problems. We
characterize datasets sampled from six classification problems by performance
ranks of simple classifiers, and define the similarity by the inverse of
Euclidean distance in this meta-feature space. We visualize the similarities in
a 2D space, where meaningful clusters start to emerge, and show that the
proposed representation can be used to classify datasets according to their
origin with 89.3\% accuracy. These findings, together with the observations of
recent trends in machine learning, suggest that meta-learning could be a
valuable tool for the medical imaging community
Domain-adversarial neural networks to address the appearance variability of histopathology images
Preparing and scanning histopathology slides consists of several steps, each
with a multitude of parameters. The parameters can vary between pathology labs
and within the same lab over time, resulting in significant variability of the
tissue appearance that hampers the generalization of automatic image analysis
methods. Typically, this is addressed with ad-hoc approaches such as staining
normalization that aim to reduce the appearance variability. In this paper, we
propose a systematic solution based on domain-adversarial neural networks. We
hypothesize that removing the domain information from the model representation
leads to better generalization. We tested our hypothesis for the problem of
mitosis detection in breast cancer histopathology images and made a comparative
analysis with two other approaches. We show that combining color augmentation
with domain-adversarial training is a better alternative than standard
approaches to improve the generalization of deep learning methods.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
We propose a method for synthesizing cardiac magnetic resonance (MR) images
with plausible heart pathologies and realistic appearances for the purpose of
generating labeled data for the application of supervised deep-learning (DL)
training. The image synthesis consists of label deformation and label-to-image
translation tasks. The former is achieved via latent space interpolation in a
VAE model, while the latter is accomplished via a label-conditional GAN model.
We devise three approaches for label manipulation in the latent space of the
trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate
the intermediate slices of a subject to increase the through-plane resolution,
ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and
appearance of intermediate images between two dissimilar subjects acquired with
different scanner vendors, and iii) \textbf{pathology synthesis} aiming to
synthesize a series of pseudo-pathological synthetic subjects with
characteristics of a desired heart disease. Furthermore, we propose to model
the relationship between 2D slices in the latent space of the VAE prior to
reconstruction for generating 3D-consistent subjects from stacking up 2D
slice-by-slice generations. We demonstrate that such an approach could provide
a solution to diversify and enrich an available database of cardiac MR images
and to pave the way for the development of generalizable DL-based image
analysis algorithms. We quantitatively evaluate the quality of the synthesized
data in an augmentation scenario to achieve generalization and robustness to
multi-vendor and multi-disease data for image segmentation. Our code is
available at https://github.com/sinaamirrajab/CardiacPathologySynthesis.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://www.melba-journal.org/2023:01
Inferring a Third Spatial Dimension from 2D Histological Images
Histological images are obtained by transmitting light through a tissue
specimen that has been stained in order to produce contrast. This process
results in 2D images of the specimen that has a three-dimensional structure. In
this paper, we propose a method to infer how the stains are distributed in the
direction perpendicular to the surface of the slide for a given 2D image in
order to obtain a 3D representation of the tissue. This inference is achieved
by decomposition of the staining concentration maps under constraints that
ensure realistic decomposition and reconstruction of the original 2D images.
Our study shows that it is possible to generate realistic 3D images making this
method a potential tool for data augmentation when training deep learning
models.Comment: IEEE International Symposium on Biomedical Imaging (ISBI), 201
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection
Out-of-distribution (OOD) detection is crucial for the safety and reliability
of artificial intelligence algorithms, especially in the medical domain. In the
context of the Medical OOD (MOOD) detection challenge 2023, we propose a
pipeline that combines a histogram-based method and a diffusion-based method.
The histogram-based method is designed to accurately detect homogeneous
anomalies in the toy examples of the challenge, such as blobs with constant
intensity values. The diffusion-based method is based on one of the latest
methods for unsupervised anomaly detection, called DDPM-OOD. We explore this
method and propose extensive post-processing steps for pixel-level and
sample-level anomaly detection on brain MRI and abdominal CT data provided by
the challenge. Our results show that the proposed DDPM method is sensitive to
blur and bias field samples, but faces challenges with anatomical deformation,
black slice, and swapped patches. These findings suggest that further research
is needed to improve the performance of DDPM for OOD detection in medical
images.Comment: 9 pages, 5 figures, submission to Medical Out-of-Distribution (MOOD)
challenge at MICCAI 202
Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs
We propose a method for synthesizing cardiac MR images with plausible heart shapes and realistic appearances for the purpose of generating labeled data for deep-learning (DL) training. It breaks down the image synthesis into label deformation and label-to-image translation tasks. The former is achieved via latent space interpolation in a VAE model, while the latter is accomplished via a conditional GAN model. We devise an approach for label manipulation in the latent space of the trained VAE model, namely pathology synthesis, aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease. Furthermore, we propose to model the relationship between 2D slices in the latent space of the VAE via estimating the correlation coefficient matrix between the latent vectors and utilizing it to correlate elements of randomly drawn samples before decoding to image space. This simple yet effective approach results in generating 3D consistent subjects from 2D slice-by-slice generations. Such an approach could provide a solution to diversify and enrich the available database of cardiac MR images and to pave the way for the development of generalizable DL-based image analysis algorithms. The code will be available at https://github.com/sinaamirrajab/CardiacPathologySynthesis
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