141 research outputs found
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Integrating high-level semantically correlated contents and low-level
anatomical features is of central importance in medical image segmentation.
Towards this end, recent deep learning-based medical segmentation methods have
shown great promise in better modeling such information. However, convolution
operators for medical segmentation typically operate on regular grids, which
inherently blur the high-frequency regions, i.e., boundary regions. In this
work, we propose MORSE, a generic implicit neural rendering framework designed
at an anatomical level to assist learning in medical image segmentation. Our
method is motivated by the fact that implicit neural representation has been
shown to be more effective in fitting complex signals and solving computer
graphics problems than discrete grid-based representation. The core of our
approach is to formulate medical image segmentation as a rendering problem in
an end-to-end manner. Specifically, we continuously align the coarse
segmentation prediction with the ambiguous coordinate-based point
representations and aggregate these features to adaptively refine the boundary
region. To parallelly optimize multi-scale pixel-level features, we leverage
the idea from Mixture-of-Expert (MoE) to design and train our MORSE with a
stochastic gating mechanism. Our experiments demonstrate that MORSE can work
well with different medical segmentation backbones, consistently achieving
competitive performance improvements in both 2D and 3D supervised medical
segmentation methods. We also theoretically analyze the superiority of MORSE.Comment: Accepted at International Conference on Medical Image Computing and
Computer-Assisted Intervention (MICCAI 2023
Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Learning spatial-temporal correspondences in cardiac motion from images is
important for understanding the underlying dynamics of cardiac anatomical
structures. Many methods explicitly impose smoothness constraints such as the
norm on the displacement vector field (DVF), while usually
ignoring biomechanical feasibility in the transformation. Other geometric
constraints either regularize specific regions of interest such as imposing
incompressibility on the myocardium or introduce additional steps such as
training a separate network-based regularizer on physically simulated datasets.
In this work, we propose an explicit biomechanics-informed prior as
regularization on the predicted DVF in modeling a more generic biomechanically
plausible transformation within all cardiac structures without introducing
additional training complexity. We validate our methods on two publicly
available datasets in the context of 2D MRI data and perform extensive
experiments to illustrate the effectiveness and robustness of our proposed
methods compared to other competing regularization schemes. Our proposed
methods better preserve biomechanical properties by visual assessment and show
advantages in segmentation performance using quantitative evaluation metrics.
The code is publicly available at
\url{https://github.com/Voldemort108X/bioinformed_reg}.Comment: Accepted by MICCAI-STACOM 2022 as an oral presentatio
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
Medical data often exhibits long-tail distributions with heavy class
imbalance, which naturally leads to difficulty in classifying the minority
classes (i.e., boundary regions or rare objects). Recent work has significantly
improved semi-supervised medical image segmentation in long-tailed scenarios by
equipping them with unsupervised contrastive criteria. However, it remains
unclear how well they will perform in the labeled portion of data where class
distribution is also highly imbalanced. In this work, we present ACTION++, an
improved contrastive learning framework with adaptive anatomical contrast for
semi-supervised medical segmentation. Specifically, we propose an adaptive
supervised contrastive loss, where we first compute the optimal locations of
class centers uniformly distributed on the embedding space (i.e., off-line),
and then perform online contrastive matching training by encouraging different
class features to adaptively match these distinct and uniformly distributed
class centers. Moreover, we argue that blindly adopting a constant temperature
in the contrastive loss on long-tailed medical data is not optimal, and
propose to use a dynamic via a simple cosine schedule to yield better
separation between majority and minority classes. Empirically, we evaluate
ACTION++ on ACDC and LA benchmarks and show that it achieves state-of-the-art
across two semi-supervised settings. Theoretically, we analyze the performance
of adaptive anatomical contrast and confirm its superiority in label
efficiency.Comment: Accepted by International Conference on Medical Image Computing and
Computer-Assisted Intervention (MICCAI 2023
Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms
Developing both graphical and commandline user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing.
We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The technological The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software - BioImage Suite (bioimagesuite.org)
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Using Perturbation Theory to Compute the Morphological Similarity of Diffusion Tensors
Computing the morphological similarity of diffusion tensors (DTs) at neighboring voxels within a DT image, or at corresponding locations across different DT images, is a fundamental and ubiquitous operation in the postprocessing of DT images. The morphological similarity of DTs typically has been computed using either the principal directions (PDs) of DTs (i.e., the direction along which water molecules diffuse preferentially) or their tensor elements. Although comparing PDs allows the similarity of one morphological feature of DTs to be visualized directly in eigenspace, this method takes into account only a single eigenvector, and it is therefore sensitive to the presence of noise in the images that can introduce error in to the estimation of that vector. Although comparing tensor elements, rather than PDs, is comparatively more robust to the effects of noise, the individual elements of a given tensor do not directly reflect the diffusion properties of water molecules. We propose a measure for computing the morphological similarity of DTs that uses both their eigenvalues and eigenvectors, and that also accounts for the noise levels present in DT images. Our measure presupposes that DTs in a homogeneous region within or across DT images are random perturbations of one another in the presence of noise. The similarity values that are computed using our method are smooth (in the sense that small changes in eigenvalues and eigenvectors cause only small changes in similarity), and they are symmetric when differences in eigenvalues and eigenvectors are also symmetric. In addition, our method does not presuppose that the corresponding eigenvectors across two DTs have been identified accurately, an assumption that is problematic in the presence of noise. Because we compute the similarity between DTs using their eigenspace components, our similarity measure relates directly to both the magnitude and the direction of the diffusion of water molecules. The favorable performance characteristics of our measure offer the prospect of substantially improving additional postprocessing operations that are commonly performed on DTI datasets, such as image segmentation, fiber tracking, noise filtering, and spatial normalization
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Many medical datasets have recently been created for medical image
segmentation tasks, and it is natural to question whether we can use them to
sequentially train a single model that (1) performs better on all these
datasets, and (2) generalizes well and transfers better to the unknown target
site domain. Prior works have achieved this goal by jointly training one model
on multi-site datasets, which achieve competitive performance on average but
such methods rely on the assumption about the availability of all training
data, thus limiting its effectiveness in practical deployment. In this paper,
we propose a novel multi-site segmentation framework called
incremental-transfer learning (ITL), which learns a model from multi-site
datasets in an end-to-end sequential fashion. Specifically, "incremental"
refers to training sequentially constructed datasets, and "transfer" is
achieved by leveraging useful information from the linear combination of
embedding features on each dataset. In addition, we introduce our ITL
framework, where we train the network including a site-agnostic encoder with
pre-trained weights and at most two segmentation decoder heads. We also design
a novel site-level incremental loss in order to generalize well on the target
domain. Second, we show for the first time that leveraging our ITL training
scheme is able to alleviate challenging catastrophic forgetting problems in
incremental learning. We conduct experiments using five challenging benchmark
datasets to validate the effectiveness of our incremental-transfer learning
approach. Our approach makes minimal assumptions on computation resources and
domain-specific expertise, and hence constitutes a strong starting point in
multi-site medical image segmentation
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