186 research outputs found
Generative modeling of living cells with SO(3)-equivariant implicit neural representations
Data-driven cell tracking and segmentation methods in biomedical imaging
require diverse and information-rich training data. In cases where the number
of training samples is limited, synthetic computer-generated data sets can be
used to improve these methods. This requires the synthesis of cell shapes as
well as corresponding microscopy images using generative models. To synthesize
realistic living cell shapes, the shape representation used by the generative
model should be able to accurately represent fine details and changes in
topology, which are common in cells. These requirements are not met by 3D voxel
masks, which are restricted in resolution, and polygon meshes, which do not
easily model processes like cell growth and mitosis. In this work, we propose
to represent living cell shapes as level sets of signed distance functions
(SDFs) which are estimated by neural networks. We optimize a fully-connected
neural network to provide an implicit representation of the SDF value at any
point in a 3D+time domain, conditioned on a learned latent code that is
disentangled from the rotation of the cell shape. We demonstrate the
effectiveness of this approach on cells that exhibit rapid deformations
(Platynereis dumerilii), cells that grow and divide (C. elegans), and cells
that have growing and branching filopodial protrusions (A549 human lung
carcinoma cells). A quantitative evaluation using shape features, Hausdorff
distance, and Dice similarity coefficients of real and synthetic cell shapes
shows that our model can generate topologically plausible complex cell shapes
in 3D+time with high similarity to real living cell shapes. Finally, we show
how microscopy images of living cells that correspond to our generated cell
shapes can be synthesized using an image-to-image model.Comment: Medical Image Analysis 2023 (Submitted
H-NeXt: The next step towards roto-translation invariant networks
The widespread popularity of equivariant networks underscores the
significance of parameter efficient models and effective use of training data.
At a time when robustness to unseen deformations is becoming increasingly
important, we present H-NeXt, which bridges the gap between equivariance and
invariance. H-NeXt is a parameter-efficient roto-translation invariant network
that is trained without a single augmented image in the training set. Our
network comprises three components: an equivariant backbone for learning
roto-translation independent features, an invariant pooling layer for
discarding roto-translation information, and a classification layer. H-NeXt
outperforms the state of the art in classification on unaugmented training sets
and augmented test sets of MNIST and CIFAR-10.Comment: Appears in British Machine Vision Conference 2023 (BMVC 2023
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction
It is time-consuming and expensive to take high-quality or high-resolution
electron microscopy (EM) and fluorescence microscopy (FM) images. Taking these
images could be even invasive to samples and may damage certain subtleties in
the samples after long or intense exposures, often necessary for achieving
high-quality or high resolution in the first place. Advances in deep learning
enable us to perform image-to-image transformation tasks for various types of
microscopy image reconstruction, computationally producing high-quality images
from the physically acquired low-quality ones. When training image-to-image
transformation models on pairs of experimentally acquired microscopy images,
prior models suffer from performance loss due to their inability to capture
inter-image dependencies and common features shared among images. Existing
methods that take advantage of shared features in image classification tasks
cannot be properly applied to image reconstruction tasks because they fail to
preserve the equivariance property under spatial permutations, something
essential in image-to-image transformation. To address these limitations, we
propose the augmented equivariant attention networks (AEANets) with better
capability to capture inter-image dependencies, while preserving the
equivariance property. The proposed AEANets captures inter-image dependencies
and shared features via two augmentations on the attention mechanism, which are
the shared references and the batch-aware attention during training. We
theoretically derive the equivariance property of the proposed augmented
attention model and experimentally demonstrate its consistent superiority in
both quantitative and visual results over the baseline methods.Comment: 11 pages, 8 figure
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
Efficient Brain Tumor Segmentation with Multiscale Two-Pathway-Group Conventional Neural Networks
© 2013 IEEE. Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive
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