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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network
Deep learning models have had a great success in disease classifications
using large data pools of skin cancer images or lung X-rays. However, data
scarcity has been the roadblock of applying deep learning models directly on
prostate multiparametric MRI (mpMRI). Although model interpretation has been
heavily studied for natural images for the past few years, there has been a
lack of interpretation of deep learning models trained on medical images. This
work designs a customized workflow for the small and imbalanced data set of
prostate mpMRI where features were extracted from a deep learning model and
then analyzed by a traditional machine learning classifier. In addition, this
work contributes to revealing how deep learning models interpret mpMRI for
prostate cancer patients stratification
UOD: Universal One-shot Detection of Anatomical Landmarks
One-shot medical landmark detection gains much attention and achieves great
success for its label-efficient training process. However, existing one-shot
learning methods are highly specialized in a single domain and suffer domain
preference heavily in the situation of multi-domain unlabeled data. Moreover,
one-shot learning is not robust that it faces performance drop when annotating
a sub-optimal image. To tackle these issues, we resort to developing a
domain-adaptive one-shot landmark detection framework for handling multi-domain
medical images, named Universal One-shot Detection (UOD). UOD consists of two
stages and two corresponding universal models which are designed as
combinations of domain-specific modules and domain-shared modules. In the first
stage, a domain-adaptive convolution model is self-supervised learned to
generate pseudo landmark labels. In the second stage, we design a
domain-adaptive transformer to eliminate domain preference and build the global
context for multi-domain data. Even though only one annotated sample from each
domain is available for training, the domain-shared modules help UOD aggregate
all one-shot samples to detect more robust and accurate landmarks. We
investigated both qualitatively and quantitatively the proposed UOD on three
widely-used public X-ray datasets in different anatomical domains (i.e., head,
hand, chest) and obtained state-of-the-art performances in each domain.Comment: Eealy accepted by MICCAI 2023. 11pages, 4 figures, 2 table
Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
Recently, the diffusion model has emerged as a superior generative model that
can produce high quality and realistic images. However, for medical image
translation, the existing diffusion models are deficient in accurately
retaining structural information since the structure details of source domain
images are lost during the forward diffusion process and cannot be fully
recovered through learned reverse diffusion, while the integrity of anatomical
structures is extremely important in medical images. For instance, errors in
image translation may distort, shift, or even remove structures and tumors,
leading to incorrect diagnosis and inadequate treatments. Training and
conditioning diffusion models using paired source and target images with
matching anatomy can help. However, such paired data are very difficult and
costly to obtain, and may also reduce the robustness of the developed model to
out-of-distribution testing data. We propose a frequency-guided diffusion model
(FGDM) that employs frequency-domain filters to guide the diffusion model for
structure-preserving image translation. Based on its design, FGDM allows
zero-shot learning, as it can be trained solely on the data from the target
domain, and used directly for source-to-target domain translation without any
exposure to the source-domain data during training. We evaluated it on three
cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and
a cross-institutional MR imaging translation task. FGDM outperformed the
state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics
of Frechet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and
Structural Similarity Index Measure (SSIM), showing its significant advantages
in zero-shot medical image translation
Dynamic CBCT Imaging using Prior Model-Free Spatiotemporal Implicit Neural Representation (PMF-STINR)
Dynamic cone-beam computed tomography (CBCT) can capture
high-spatial-resolution, time-varying images for motion monitoring, patient
setup, and adaptive planning of radiotherapy. However, dynamic CBCT
reconstruction is an extremely ill-posed spatiotemporal inverse problem, as
each CBCT volume in the dynamic sequence is only captured by one or a few X-ray
projections. We developed a machine learning-based technique, prior-model-free
spatiotemporal implicit neural representation (PMF-STINR), to reconstruct
dynamic CBCTs from sequentially acquired X-ray projections. PMF-STINR employs a
joint image reconstruction and registration approach to address the
under-sampling challenge. Specifically, PMF-STINR uses spatial implicit neural
representation to reconstruct a reference CBCT volume, and it applies temporal
INR to represent the intra-scan dynamic motion with respect to the reference
CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a
learning-based B-spline motion model to capture time-varying deformable motion
during the reconstruction. Compared with previous methods, the spatial INR, the
temporal INR, and the B-spline model of PMF-STINR are all learned on the fly
during reconstruction in a one-shot fashion, without using any patient-specific
prior knowledge or motion sorting/binning. PMF-STINR was evaluated via digital
phantom simulations, physical phantom measurements, and a multi-institutional
patient dataset featuring various imaging protocols (half-fan/full-fan, full
sampling/sparse sampling, different energy and mAs settings, etc.). The results
showed that the one-shot learning-based PMF-STINR can accurately and robustly
reconstruct dynamic CBCTs and capture highly irregular motion with high
temporal (~0.1s) resolution and sub-millimeter accuracy. It can be a promising
tool for motion management by offering richer motion information than
traditional 4D-CBCTs
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