69 research outputs found
Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is
indispensable in medical image analysis and clinical research. A reliable and
effective reconstruction tool should: be fast in prediction of accurate well
localised and high resolution models, evaluate prediction uncertainty, work
with as little input data as possible. Current deep learning state of the art
(SOTA) 3D reconstruction methods, however, often only produce shapes of limited
variability positioned in a canonical position or lack uncertainty evaluation.
In this paper, we present a novel probabilistic deep learning approach for
concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric
uncertainty prediction. Our method is capable of reconstructing large surface
meshes from three quasi-orthogonal MR imaging slices from limited training sets
whilst modelling the location of each mesh vertex through a Gaussian
distribution. Prior shape information is encoded using a built-in linear
principal component analysis (PCA) model. Extensive experiments on cardiac MR
data show that our probabilistic approach successfully assesses prediction
uncertainty while at the same time qualitatively and quantitatively outperforms
SOTA methods in shape prediction. Compared to SOTA, we are capable of properly
localising and orientating the prediction via the use of a spatially aware
neural network.Comment: MICCAI 202
Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation
Semi-supervised learning has greatly advanced medical image segmentation
since it effectively alleviates the need of acquiring abundant annotations from
experts and utilizes unlabeled data which is much easier to acquire. Among
existing perturbed consistency learning methods, mean-teacher model serves as a
standard baseline for semi-supervised medical image segmentation. In this
paper, we present a simple yet efficient competitive ensembling teacher student
framework for semi-supervised for left atrium segmentation from 3D MR images,
in which two student models with different task-level disturbances are
introduced to learn mutually, while a competitive ensembling strategy is
performed to ensemble more reliable information to teacher model. Different
from the one-way transfer between teacher and student models, our framework
facilitates the collaborative learning procedure of different student models
with the guidance of teacher model and motivates different training networks
for a competitive learning and ensembling procedure to achieve better
performance. We evaluate our proposed method on the public Left Atrium (LA)
dataset and it obtains impressive performance gains by exploiting the unlabeled
data effectively and outperforms several existing semi-supervised methods.Comment: Accepeted for BIBM 202
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
Self-training with dual uncertainty for semi-supervised medical image segmentation
In the field of semi-supervised medical image segmentation, the shortage of
labeled data is the fundamental problem. How to effectively learn image
features from unlabeled images to improve segmentation accuracy is the main
research direction in this field. Traditional self-training methods can
partially solve the problem of insufficient labeled data by generating pseudo
labels for iterative training. However, noise generated due to the model's
uncertainty during training directly affects the segmentation results.
Therefore, we added sample-level and pixel-level uncertainty to stabilize the
training process based on the self-training framework. Specifically, we saved
several moments of the model during pre-training, and used the difference
between their predictions on unlabeled samples as the sample-level uncertainty
estimate for that sample. Then, we gradually add unlabeled samples from easy to
hard during training. At the same time, we added a decoder with different
upsampling methods to the segmentation network and used the difference between
the outputs of the two decoders as pixel-level uncertainty. In short, we
selectively retrained unlabeled samples and assigned pixel-level uncertainty to
pseudo labels to optimize the self-training process. We compared the
segmentation results of our model with five semi-supervised approaches on the
public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method
achieves better segmentation performance on both datasets under the same
settings, demonstrating its effectiveness, robustness, and potential
transferability to other medical image segmentation tasks. Keywords: Medical
image segmentation, semi-supervised learning, self-training, uncertainty
estimatio
A Multi-scale Learning of Data-driven and Anatomically Constrained Image Registration for Adult and Fetal Echo Images
Temporal echo image registration is a basis for clinical quantifications such
as cardiac motion estimation, myocardial strain assessments, and stroke volume
quantifications. Deep learning image registration (DLIR) is consistently
accurate, requires less computing effort, and has shown encouraging results in
earlier applications. However, we propose that a greater focus on the warped
moving image's anatomic plausibility and image quality can support robust DLIR
performance. Further, past implementations have focused on adult echo, and
there is an absence of DLIR implementations for fetal echo. We propose a
framework combining three strategies for DLIR for both fetal and adult echo:
(1) an anatomic shape-encoded loss to preserve physiological myocardial and
left ventricular anatomical topologies in warped images; (2) a data-driven loss
that is trained adversarially to preserve good image texture features in warped
images; and (3) a multi-scale training scheme of a data-driven and anatomically
constrained algorithm to improve accuracy. Our experiments show that the
shape-encoded loss and the data-driven adversarial loss are strongly correlated
to good anatomical topology and image textures, respectively. They improve
different aspects of registration performance in a non-overlapping way,
justifying their combination. We show that these strategies can provide
excellent registration results in both adult and fetal echo using the publicly
available CAMUS adult echo dataset and our private multi-demographic fetal echo
dataset, despite fundamental distinctions between adult and fetal echo images.
Our approach also outperforms traditional non-DL gold standard registration
approaches, including Optical Flow and Elastix. Registration improvements could
also be translated to more accurate and precise clinical quantification of
cardiac ejection fraction, demonstrating a potential for translation
Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images
Organ at Risk (OAR) segmentation from CT scans is a key component of the
radiotherapy treatment workflow. In recent years, deep learning techniques have
shown remarkable potential in automating this process. In this paper, we
investigate the performance of Generative Adversarial Networks (GANs) compared
to supervised learning approaches for segmenting OARs from CT images. We
propose three GAN-based models with identical generator architectures but
different discriminator networks. These models are compared with
well-established CNN models, such as SE-ResUnet and DeepLabV3, using the
StructSeg dataset, which consists of 50 annotated CT scans containing contours
of six OARs. Our work aims to provide insight into the advantages and
disadvantages of adversarial training in the context of OAR segmentation. The
results are very promising and show that the proposed GAN-based approaches are
similar or superior to their CNN-based counterparts, particularly when
segmenting more challenging target organs
Weakly supervised medical image segmentation through dense combinations of dense pseudo-l-abels
Annotating a large amount of medical imaging data thoroughly for training purposes can be expensive, particularly for medical image segmentation tasks; whereas obtaining scribbles, a less precise
form of annotation, is more feasible for clinicians. Nevertheless, training semantic segmentation networks with limited-signal supervision remains a technical challenge. In this paper, we present an innovative
scribble-supervised image segmentation via densely ensembling dense
pseudos called Collaborative Hybrid Networks(CHNets), which consists
of groups of CNN- and ViT-based segmentation networks. A simple yet
efficient densely collaboration scheme is introduced to ensemble dense
pseudo label to expand dataset allowing full-signal supervision. Additionally, internal consistency and external consistency training among
networks are proposed to ensure that each network is beneficial to the
other, resulting in a significant improvement. Our experiments on a public MRI benchmark dataset demonstrate that our proposed approach
outperforms other weakly-supervised methods on various metrics
MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement
Scoliosis diagnosis and assessment depend largely on the measurement of the
Cobb angle in spine X-ray images. With the emergence of deep learning
techniques that employ landmark detection, tilt prediction, and spine
segmentation, automated Cobb angle measurement has become increasingly popular.
However, these methods encounter difficulties such as high noise sensitivity,
intricate computational procedures, and exclusive reliance on a single type of
morphological information. In this paper, we introduce the Multiple
Morphology-Aware Network (MMA-Net), a novel framework that improves Cobb angle
measurement accuracy by integrating multiple spine morphology as attention
information. In the MMA-Net, we first feed spine X-ray images into the
segmentation network to produce multiple morphological information (spine
region, centerline, and boundary) and then concatenate the original X-ray image
with the resulting segmentation maps as input for the regression module to
perform precise Cobb angle measurement. Furthermore, we devise joint loss
functions for our segmentation and regression network training, respectively.
We evaluate our method on the AASCE challenge dataset and achieve superior
performance with the SMAPE of 7.28% and the MAE of 3.18{\deg}, indicating a
strong competitiveness compared to other outstanding methods. Consequently, we
can offer clinicians automated, efficient, and reliable Cobb angle measurement
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