38 research outputs found
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
Semantic segmentation is essentially important to biomedical image analysis.
Many recent works mainly focus on integrating the Fully Convolutional Network
(FCN) architecture with sophisticated convolution implementation and deep
supervision. In this paper, we propose to decompose the single segmentation
task into three subsequent sub-tasks, including (1) pixel-wise image
segmentation, (2) prediction of the class labels of the objects within the
image, and (3) classification of the scene the image belonging to. While these
three sub-tasks are trained to optimize their individual loss functions of
different perceptual levels, we propose to let them interact by the task-task
context ensemble. Moreover, we propose a novel sync-regularization to penalize
the deviation between the outputs of the pixel-wise segmentation and the class
prediction tasks. These effective regularizations help FCN utilize context
information comprehensively and attain accurate semantic segmentation, even
though the number of the images for training may be limited in many biomedical
applications. We have successfully applied our framework to three diverse 2D/3D
medical image datasets, including Robotic Scene Segmentation Challenge 18
(ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus
Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all
three challenges.Comment: IEEE Transactions on Medical Imagin
Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening
Vulvovaginal candidiasis (VVC) is the most prevalent human candidal
infection, estimated to afflict approximately 75% of all women at least once in
their lifetime. It will lead to several symptoms including pruritus, vaginal
soreness, and so on. Automatic whole slide image (WSI) classification is highly
demanded, for the huge burden of disease control and prevention. However, the
WSI-based computer-aided VCC screening method is still vacant due to the scarce
labeled data and unique properties of candida. Candida in WSI is challenging to
be captured by conventional classification models due to its distinctive
elongated shape, the small proportion of their spatial distribution, and the
style gap from WSIs. To make the model focus on the candida easier, we propose
an attention-guided method, which can obtain a robust diagnosis classification
model. Specifically, we first use a pre-trained detection model as prior
instruction to initialize the classification model. Then we design a Skip
Self-Attention module to refine the attention onto the fined-grained features
of candida. Finally, we use a contrastive learning method to alleviate the
overfitting caused by the style gap of WSIs and suppress the attention to false
positive regions. Our experimental results demonstrate that our framework
achieves state-of-the-art performance. Code and example data are available at
https://github.com/cjdbehumble/MICCAI2023-VVC-Screening.Comment: Accepted in the main conference MICCAI 202
CT-based Subchondral Bone Microstructural Analysis in Knee Osteoarthritis via MR-Guided Distillation Learning
Background: MR-based subchondral bone effectively predicts knee
osteoarthritis. However, its clinical application is limited by the cost and
time of MR. Purpose: We aim to develop a novel distillation-learning-based
method named SRRD for subchondral bone microstructural analysis using
easily-acquired CT images, which leverages paired MR images to enhance the
CT-based analysis model during training. Materials and Methods: Knee joint
images of both CT and MR modalities were collected from October 2020 to May
2021. Firstly, we developed a GAN-based generative model to transform MR images
into CT images, which was used to establish the anatomical correspondence
between the two modalities. Next, we obtained numerous patches of subchondral
bone regions of MR images, together with their trabecular parameters (BV / TV,
Tb. Th, Tb. Sp, Tb. N) from the corresponding CT image patches via regression.
The distillation-learning technique was used to train the regression model and
transfer MR structural information to the CT-based model. The regressed
trabecular parameters were further used for knee osteoarthritis classification.
Results: A total of 80 participants were evaluated. CT-based regression results
of trabecular parameters achieved intra-class correlation coefficients (ICCs)
of 0.804, 0.773, 0.711, and 0.622 for BV / TV, Tb. Th, Tb. Sp, and Tb. N,
respectively. The use of distillation learning significantly improved the
performance of the CT-based knee osteoarthritis classification method using the
CNN approach, yielding an AUC score of 0.767 (95% CI, 0.681-0.853) instead of
0.658 (95% CI, 0.574-0.742) (p<.001). Conclusions: The proposed SRRD method
showed high reliability and validity in MR-CT registration, regression, and
knee osteoarthritis classification, indicating the feasibility of subchondral
bone microstructural analysis based on CT images.Comment: 5 figures, 4 table
Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images
Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. First, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. Second, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. Furthermore, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently
Brain atlas fusion from high-thickness diagnostic magnetic resonance images by learning-based super-resolution
It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images
RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation
Medical image segmentation methods are generally designed as fully-supervised
to guarantee model performance, which require a significant amount of expert
annotated samples that are high-cost and laborious. Semi-supervised image
segmentation can alleviate the problem by utilizing a large number of unlabeled
images along with limited labeled images. However, learning a robust
representation from numerous unlabeled images remains challenging due to
potential noise in pseudo labels and insufficient class separability in feature
space, which undermines the performance of current semi-supervised segmentation
approaches. To address the issues above, we propose a novel semi-supervised
segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS),
which combines a rectified pseudo supervision and voxel-level contrastive
learning to improve the effectiveness of semi-supervised segmentation.
Particularly, we design a novel rectification strategy for the pseudo
supervision method based on uncertainty estimation and consistency
regularization to reduce the noise influence in pseudo labels. Furthermore, we
introduce a bidirectional voxel contrastive loss to the network to ensure
intra-class consistency and inter-class contrast in feature space, which
increases class separability in the segmentation. The proposed RCPS
segmentation method has been validated on two public datasets and an in-house
clinical dataset. Experimental results reveal that the proposed method yields
better segmentation performance compared with the state-of-the-art methods in
semi-supervised medical image segmentation. The source code is available at
https://github.com/hsiangyuzhao/RCPS
AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation
Accurate automatic segmentation of medical images typically requires large
datasets with high-quality annotations, making it less applicable in clinical
settings due to limited training data. One-shot segmentation based on learned
transformations (OSSLT) has shown promise when labeled data is extremely
limited, typically including unsupervised deformable registration, data
augmentation with learned registration, and segmentation learned from augmented
data. However, current one-shot segmentation methods are challenged by limited
data diversity during augmentation, and potential label errors caused by
imperfect registration. To address these issues, we propose a novel one-shot
medical image segmentation method with adversarial training and label error
rectification (AdLER), with the aim of improving the diversity of generated
data and correcting label errors to enhance segmentation performance.
Specifically, we implement a novel dual consistency constraint to ensure
anatomy-aligned registration that lessens registration errors. Furthermore, we
develop an adversarial training strategy to augment the atlas image, which
ensures both generation diversity and segmentation robustness. We also propose
to rectify potential label errors in the augmented atlas images by estimating
segmentation uncertainty, which can compensate for the imperfect nature of
deformable registration and improve segmentation authenticity. Experiments on
the CANDI and ABIDE datasets demonstrate that the proposed AdLER outperforms
previous state-of-the-art methods by 0.7% (CANDI), 3.6% (ABIDE "seen"), and
4.9% (ABIDE "unseen") in segmentation based on Dice scores, respectively. The
source code will be available at https://github.com/hsiangyuzhao/AdLER
Automatic labeling of MR brain images by hierarchical learning of atlas forests: Automatic labeling of MR brain images
Automatic brain image labeling is highly demanded in the field of medical image analysis. Multiatlas-based approaches are widely used due to their simplicity and robustness in applications. Also, random forest technique is recognized as an efficient method for labeling, although there are several existing limitations. In this paper, the authors intend to address those limitations by proposing a novel framework based on the hierarchical learning of atlas forests