18 research outputs found
-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
This paper presents a generic probabilistic framework for estimating the
statistical dependency and finding the anatomical correspondences among an
arbitrary number of medical images. The method builds on a novel formulation of
the -dimensional joint intensity distribution by representing the common
anatomy as latent variables and estimating the appearance model with
nonparametric estimators. Through connection to maximum likelihood and the
expectation-maximization algorithm, an information\hyp{}theoretic metric called
-metric and a co-registration algorithm named -CoReg
are induced, allowing groupwise registration of the observed images with
computational complexity of . Moreover, the method naturally
extends for a weakly-supervised scenario where anatomical labels of certain
images are provided. This leads to a combined\hyp{}computing framework
implemented with deep learning, which performs registration and segmentation
simultaneously and collaboratively in an end-to-end fashion. Extensive
experiments were conducted to demonstrate the versatility and applicability of
our model, including multimodal groupwise registration, motion correction for
dynamic contrast enhanced magnetic resonance images, and deep combined
computing for multimodal medical images. Results show the superiority of our
method in various applications in terms of both accuracy and efficiency,
highlighting the advantage of the proposed representation of the imaging
process
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement
Multimodal groupwise registration aligns internal structures in a group of
medical images. Current approaches to this problem involve developing
similarity measures over the joint intensity profile of all images, which may
be computationally prohibitive for large image groups and unstable under
various conditions. To tackle these issues, we propose BInGo, a general
unsupervised hierarchical Bayesian framework based on deep learning, to learn
intrinsic structural representations to measure the similarity of multimodal
images. Particularly, a variational auto-encoder with a novel posterior is
proposed, which facilitates the disentanglement learning of structural
representations and spatial transformations, and characterizes the imaging
process from the common structure with shape transition and appearance
variation. Notably, BInGo is scalable to learn from small groups, whereas being
tested for large-scale groupwise registration, thus significantly reducing
computational costs. We compared BInGo with five iterative or deep learning
methods on three public intrasubject and intersubject datasets, i.e. BraTS,
MS-CMR of the heart, and Learn2Reg abdomen MR-CT, and demonstrated its superior
accuracy and computational efficiency, even for very large group sizes (e.g.,
over 1300 2D images from MS-CMR in each group)
MyoPS A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment
management of patients suffering from myocardial infarction, and classification
of pathology on myocardium is the key to this assessment. This work defines a
new task of medical image analysis, i.e., to perform myocardial pathology
segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR)
images, which was first proposed in the MyoPS challenge, in conjunction with
MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images,
allowing algorithms to combine the complementary information from the three CMR
sequences for pathology segmentation. In this article, we provide details of
the challenge, survey the works from fifteen participants and interpret their
methods according to five aspects, i.e., preprocessing, data augmentation,
learning strategy, model architecture and post-processing. In addition, we
analyze the results with respect to different factors, in order to examine the
key obstacles and explore potential of solutions, as well as to provide a
benchmark for future research. We conclude that while promising results have
been reported, the research is still in the early stage, and more in-depth
exploration is needed before a successful application to the clinics. Note that
MyoPS data and evaluation tool continue to be publicly available upon
registration via its homepage
(www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/)
Automatic Relation-aware Graph Network Proliferation
Graph neural architecture search has sparked much attention as Graph Neural
Networks (GNNs) have shown powerful reasoning capability in many relational
tasks. However, the currently used graph search space overemphasizes learning
node features and neglects mining hierarchical relational information.
Moreover, due to diverse mechanisms in the message passing, the graph search
space is much larger than that of CNNs. This hinders the straightforward
application of classical search strategies for exploring complicated graph
search space. We propose Automatic Relation-aware Graph Network Proliferation
(ARGNP) for efficiently searching GNNs with a relation-guided message passing
mechanism. Specifically, we first devise a novel dual relation-aware graph
search space that comprises both node and relation learning operations. These
operations can extract hierarchical node/relational information and provide
anisotropic guidance for message passing on a graph. Second, analogous to cell
proliferation, we design a network proliferation search paradigm to
progressively determine the GNN architectures by iteratively performing network
division and differentiation. The experiments on six datasets for four graph
learning tasks demonstrate that GNNs produced by our method are superior to the
current state-of-the-art hand-crafted and search-based GNNs. Codes are
available at https://github.com/phython96/ARGNP.Comment: Accepted by CVPR2022 (Oral
Numerical Simulation of the Tar Mist and Dust Movement Process in a Low-Temperature Dry Distillation Furnace
In the low-temperature dry distillation of low-rank coal, the important liquid product of coal tar is produced, but its quality and utilization rate are degraded by entrained dust. The movement of coal tar and dust in the furnace is a key factor in causing particles such as dust to mix with coal tar. Therefore, the Euler–Lagrangian method is used to simulate the two-phase motion process of gas, tar, and dust in a furnace. By considering the effects of tar particle size, dust particle size, gas velocity, tar density, and dust density, the motion process mechanism is revealed, enabling the dust content in coal tar to be reduced and the quality improved. The results indicate that tar particles with sizes less than 0.20 mm can be removed from the furnace by gas, and the smaller the particle size is, the shorter the time required for removal. Dust particles greater than 0.18 mm in size cannot be completely removed from the furnace. As the gas velocity increases, the time required for complete removal of the tar mist and dust gradually decreases. When the speed is 0.70 m/s, all tar mist is removed, although some particles remain. Tar mist with a density of more than 900 kg/m3 can be extensively removed, but dust with a density of more than 1400 kg/m3 is difficult to remove and remains in the furnace. Finally, particle size distribution experiments in the product were conducted to verify the accuracy of the numerical simulation