65 research outputs found
A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond
Over the past decade, deep learning technologies have greatly advanced the
field of medical image registration. The initial developments, such as
ResNet-based and U-Net-based networks, laid the groundwork for deep
learning-driven image registration. Subsequent progress has been made in
various aspects of deep learning-based registration, including similarity
measures, deformation regularizations, and uncertainty estimation. These
advancements have not only enriched the field of deformable image registration
but have also facilitated its application in a wide range of tasks, including
atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D
registration. In this paper, we present a comprehensive overview of the most
recent advancements in deep learning-based image registration. We begin with a
concise introduction to the core concepts of deep learning-based image
registration. Then, we delve into innovative network architectures, loss
functions specific to registration, and methods for estimating registration
uncertainty. Additionally, this paper explores appropriate evaluation metrics
for assessing the performance of deep learning models in registration tasks.
Finally, we highlight the practical applications of these novel techniques in
medical imaging and discuss the future prospects of deep learning-based image
registration
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the
developing brain but is not suitable for second-trimester anomaly screening,
for which ultrasound (US) is employed. Although expert sonographers are adept
at reading US images, MR images which closely resemble anatomical images are
much easier for non-experts to interpret. Thus in this paper we propose to
generate MR-like images directly from clinical US images. In medical image
analysis such a capability is potentially useful as well, for instance for
automatic US-MRI registration and fusion. The proposed model is end-to-end
trainable and self-supervised without any external annotations. Specifically,
based on an assumption that the US and MRI data share a similar anatomical
latent space, we first utilise a network to extract the shared latent features,
which are then used for MRI synthesis. Since paired data is unavailable for our
study (and rare in practice), pixel-level constraints are infeasible to apply.
We instead propose to enforce the distributions to be statistically
indistinguishable, by adversarial learning in both the image domain and feature
space. To regularise the anatomical structures between US and MRI during
synthesis, we further propose an adversarial structural constraint. A new
cross-modal attention technique is proposed to utilise non-local spatial
information, by encouraging multi-modal knowledge fusion and propagation. We
extend the approach to consider the case where 3D auxiliary information (e.g.,
3D neighbours and a 3D location index) from volumetric data is also available,
and show that this improves image synthesis. The proposed approach is evaluated
quantitatively and qualitatively with comparison to real fetal MR images and
other approaches to synthesis, demonstrating its feasibility of synthesising
realistic MR images.Comment: IEEE Transactions on Medical Imaging 202
EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification
The timing of cell divisions in early embryos during the In-Vitro
Fertilization (IVF) process is a key predictor of embryo viability. However,
observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consuming
process and highly depends on experts. In this paper, we propose EmbryosFormer,
a computational model to automatically detect and classify cell divisions from
original time-lapse images. Our proposed network is designed as an
encoder-decoder deformable transformer with collaborative heads. The
transformer contracting path predicts per-image labels and is optimized by a
classification head. The transformer expanding path models the temporal
coherency between embryo images to ensure monotonic non-decreasing constraint
and is optimized by a segmentation head. Both contracting and expanding paths
are synergetically learned by a collaboration head. We have benchmarked our
proposed EmbryosFormer on two datasets: a public dataset with mouse embryos
with 8-cell stage and an in-house dataset with human embryos with 4-cell stage.
Source code: https://github.com/UARK-AICV/Embryos.Comment: Accepted at WACV 202
Machine Learning in Medical Image Analysis
Machine learning is playing a pivotal role in medical image analysis. Many algorithms based on machine learning have been applied in medical imaging to solve classification, detection, and segmentation problems. Particularly, with the wide application of deep learning approaches, the performance of medical image analysis has been significantly improved. In this thesis, we investigate machine learning methods for two key challenges in medical image analysis: The first one is segmentation of medical images. The second one is learning with weak supervision in the context of medical imaging.
The first main contribution of the thesis is a series of novel approaches for image segmentation. First, we propose a framework based on multi-scale image patches and random forests to segment small vessel disease (SVD) lesions on computed tomography (CT) images. This framework is validated in terms of spatial similarity, estimated lesion volumes, visual score ratings and was compared with human experts. The results showed that the proposed framework performs as well as human experts. Second, we propose a generic convolutional neural network (CNN) architecture called the DRINet for medical image segmentation. The DRINet approach is robust in three different types of segmentation tasks, which are multi-class cerebrospinal fluid (CSF) segmentation on brain CT images, multi-organ segmentation on abdomen CT images, and multi-class tumour segmentation on brain magnetic resonance
(MR) images. Finally, we propose a CNN-based framework to segment acute ischemic lesions on diffusion weighted (DW)-MR images, where the lesions are highly variable in terms of position, shape, and size. Promising results were achieved on a large clinical dataset.
The second main contribution of the thesis is two novel strategies for learning with weak supervision. First, we propose a novel strategy called context restoration to make use of the images without annotations. The context restoration strategy is a proxy learning process based on the CNN, which extracts semantic features from images without using annotations. It was validated on classification, localization, and segmentation problems and was superior to existing strategies. Second, we propose a patch-based framework using multi-instance learning to distinguish normal and abnormal SVD on CT images, where there are only coarse-grained labels available. Our framework was observed to work better than classic methods and clinical practice.Open Acces
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability
of such methods grows, especially in high-stakes decision making areas such as
medical image analysis. This survey presents an overview of eXplainable
Artificial Intelligence (XAI) used in deep learning-based medical image
analysis. A framework of XAI criteria is introduced to classify deep
learning-based medical image analysis methods. Papers on XAI techniques in
medical image analysis are then surveyed and categorized according to the
framework and according to anatomical location. The paper concludes with an
outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho
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