76,487 research outputs found
CATS v2: Hybrid encoders for robust medical segmentation
Convolutional Neural Networks (CNNs) have exhibited strong performance in
medical image segmentation tasks by capturing high-level (local) information,
such as edges and textures. However, due to the limited field of view of
convolution kernel, it is hard for CNNs to fully represent global information.
Recently, transformers have shown good performance for medical image
segmentation due to their ability to better model long-range dependencies.
Nevertheless, transformers struggle to capture high-level spatial features as
effectively as CNNs. A good segmentation model should learn a better
representation from local and global features to be both precise and
semantically accurate. In our previous work, we proposed CATS, which is a
U-shaped segmentation network augmented with transformer encoder. In this work,
we further extend this model and propose CATS v2 with hybrid encoders.
Specifically, hybrid encoders consist of a CNN-based encoder path paralleled to
a transformer path with a shifted window, which better leverage both local and
global information to produce robust 3D medical image segmentation. We fuse the
information from the convolutional encoder and the transformer at the skip
connections of different resolutions to form the final segmentation. The
proposed method is evaluated on two public challenge datasets: Cross-Modality
Domain Adaptation (CrossMoDA) and task 5 of Medical Segmentation Decathlon
(MSD-5), to segment vestibular schwannoma (VS) and prostate, respectively.
Compared with the state-of-the-art methods, our approach demonstrates superior
performance in terms of higher Dice scores
Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
Pretraining CNN models (i.e., UNet) through self-supervision has become a
powerful approach to facilitate medical image segmentation under low annotation
regimes. Recent contrastive learning methods encourage similar global
representations when the same image undergoes different transformations, or
enforce invariance across different image/patch features that are intrinsically
correlated. However, CNN-extracted global and local features are limited in
capturing long-range spatial dependencies that are essential in biological
anatomy. To this end, we present a keypoint-augmented fusion layer that
extracts representations preserving both short- and long-range self-attention.
In particular, we augment the CNN feature map at multiple scales by
incorporating an additional input that learns long-range spatial self-attention
among localized keypoint features. Further, we introduce both global and local
self-supervised pretraining for the framework. At the global scale, we obtain
global representations from both the bottleneck of the UNet, and by aggregating
multiscale keypoint features. These global features are subsequently
regularized through image-level contrastive objectives. At the local scale, we
define a distance-based criterion to first establish correspondences among
keypoints and encourage similarity between their features. Through extensive
experiments on both MRI and CT segmentation tasks, we demonstrate the
architectural advantages of our proposed method in comparison to both CNN and
Transformer-based UNets, when all architectures are trained with randomly
initialized weights. With our proposed pretraining strategy, our method further
outperforms existing SSL methods by producing more robust self-attention and
achieving state-of-the-art segmentation results. The code is available at
https://github.com/zshyang/kaf.git.Comment: Camera ready for NeurIPS 2023. Code available at
https://github.com/zshyang/kaf.gi
Optic nerve head segmentation
Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred image
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Stratified decision forests for accurate anatomical landmark localization in cardiac images
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy
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