77 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
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
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
Effect of latent space distribution on the segmentation of images with multiple annotations
We propose the Generalized Probabilistic U-Net, which extends the
Probabilistic U-Net by allowing more general forms of the Gaussian distribution
as the latent space distribution that can better approximate the uncertainty in
the reference segmentations. We study the effect the choice of latent space
distribution has on capturing the variation in the reference segmentations for
lung tumors and white matter hyperintensities in the brain. We show that the
choice of distribution affects the sample diversity of the predictions and
their overlap with respect to the reference segmentations. We have made our
implementation available at
https://github.com/ishaanb92/GeneralizedProbabilisticUNetComment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://melba-journal.org/2023:005. arXiv admin
note: text overlap with arXiv:2207.1287
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
Rotation-invariance is a desired property of machine-learning models for
medical image analysis and in particular for computational pathology
applications. We propose a framework to encode the geometric structure of the
special Euclidean motion group SE(2) in convolutional networks to yield
translation and rotation equivariance via the introduction of SE(2)-group
convolution layers. This structure enables models to learn feature
representations with a discretized orientation dimension that guarantees that
their outputs are invariant under a discrete set of rotations. Conventional
approaches for rotation invariance rely mostly on data augmentation, but this
does not guarantee the robustness of the output when the input is rotated. At
that, trained conventional CNNs may require test-time rotation augmentation to
reach their full capability. This study is focused on histopathology image
analysis applications for which it is desirable that the arbitrary global
orientation information of the imaged tissues is not captured by the machine
learning models. The proposed framework is evaluated on three different
histopathology image analysis tasks (mitosis detection, nuclei segmentation and
tumor classification). We present a comparative analysis for each problem and
show that consistent increase of performances can be achieved when using the
proposed framework
Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks
Purpose: We developed a method to automatically locate and quantify graft
detachment after Descemet's Membrane Endothelial Keratoplasty (DMEK) in
Anterior Segment Optical Coherence Tomography (AS-OCT) scans. Methods: 1280
AS-OCT B-scans were annotated by a DMEK expert. Using the annotations, a deep
learning pipeline was developed to localize scleral spur, center the AS-OCT
B-scans and segment the detached graft sections. Detachment segmentation model
performance was evaluated per B-scan by comparing (1) length of detachment and
(2) horizontal projection of the detached sections with the expert annotations.
Horizontal projections were used to construct graft detachment maps. All final
evaluations were done on a test set that was set apart during training of the
models. A second DMEK expert annotated the test set to determine inter-rater
performance. Results: Mean scleral spur localization error was 0.155 mm,
whereas the inter-rater difference was 0.090 mm. The estimated graft detachment
lengths were in 69% of the cases within a 10-pixel (~150{\mu}m) difference from
the ground truth (77% for the second DMEK expert). Dice scores for the
horizontal projections of all B-scans with detachments were 0.896 and 0.880 for
our model and the second DMEK expert respectively. Conclusion: Our deep
learning model can be used to automatically and instantly localize graft
detachment in AS-OCT B-scans. Horizontal detachment projections can be
determined with the same accuracy as a human DMEK expert, allowing for the
construction of accurate graft detachment maps. Translational Relevance:
Automated localization and quantification of graft detachment can support DMEK
research and standardize clinical decision making.Comment: To be published in Translational Vision Science & Technolog
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