115,603 research outputs found
SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation
Automated medical image segmentation is becoming increasingly crucial to
modern clinical practice, driven by the growing demand for precise diagnosis,
the push towards personalized treatment plans, and the advancements in machine
learning algorithms, especially the incorporation of deep learning methods.
While convolutional neural networks (CNN) have been prevalent among these
methods, the remarkable potential of Transformer-based models for computer
vision tasks is gaining more acknowledgment. To harness the advantages of both
CNN-based and Transformer-based models, we propose a simple yet effective
UNet-Transformer (seUNet-Trans) model for medical image segmentation. In our
approach, the UNet model is designed as a feature extractor to generate
multiple feature maps from the input images, then the maps are propagated into
a bridge layer, which is introduced to sequentially connect the UNet and the
Transformer. In this stage, we approach the pixel-level embedding technique
without position embedding vectors, aiming to make the model more efficient.
Moreover, we apply spatial-reduction attention in the Transformer to reduce the
computational/memory overhead. By leveraging the UNet architecture and the
self-attention mechanism, our model not only retains the preservation of both
local and global context information but also is capable of capturing
long-range dependencies between input elements. The proposed model is
extensively experimented on seven medical image segmentation datasets including
polyp segmentation to demonstrate its efficacy. Comparison with several
state-of-the-art segmentation models on these datasets shows the superior
performance of our proposed seUNet-Trans network
Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation
We propose MisMatch, a novel consistency-driven semi-supervised segmentation
framework which produces predictions that are invariant to learnt feature
perturbations. MisMatch consists of an encoder and a two-head decoders. One
decoder learns positive attention to the foreground regions of interest (RoI)
on unlabelled images thereby generating dilated features. The other decoder
learns negative attention to the foreground on the same unlabelled images
thereby generating eroded features. We then apply a consistency regularisation
on the paired predictions. MisMatch outperforms state-of-the-art
semi-supervised methods on a CT-based pulmonary vessel segmentation task and a
MRI-based brain tumour segmentation task. In addition, we show that the
effectiveness of MisMatch comes from better model calibration than its
supervised learning counterpart
A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields
Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context,
the present paper proposes an unsupervised and graph-based method of image segmentation, which is
driven by an application goal, namely, the generation of image segments associated with a user-defined
and application-specific goal. A graph, together with a random grid of source elements, is defined on
top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation
algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring
the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical
modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized
through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven
segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not
require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven
predicate and a suited parametric model for the data. In the experimental validation with both magnetic
resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated
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A Hybrid Energy Model for Region Based Curve Evolution - Application to CTA Coronary Segmentation
Background and Objective: State-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation. In this work, we propose a hybrid region-based energy formulation for effective segmentation in computed tomography angiography (CTA) imagery.
Methods: The proposed hybrid energy couples an intensity-based local term with an efficient discontinuity-based global model of the image for optimal segmentation. The segmentation is achieved using a level set formulation due to the computational robustness. After validating the statistical significance of the hybrid energy, we applied the proposed model to solve an important clinical problem of 3D coronary segmentation. An improved seed detection method is used to initialize the level set evolution. Moreover, we employed an auto-correction feature that captures the emerging peripheries during the curve evolution for completeness of the coronary tree.
Results: We evaluated the segmentation accuracy of the proposed energy model against the existing techniques in two stages. Qualitative and quantitative results demonstrate the effectiveness of the proposed framework with a consistent mean sensitivity and specificity measures of 80% across the CTA data. Moreover, a high degree of agreement with respect to the inter-observer differences justifies the generalization of the proposed method.
Conclusions: The proposed method is effective to segment the coronary tree from the CTA volume based on hybrid image based energy, which can improve the clinicians ability to detect arterial abnormalities
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation
Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial
for brain tumor diagnosis, cancer management and research purposes. With the
great success of the ten-year BraTS challenges as well as the advances of CNN
and Transformer algorithms, a lot of outstanding BTS models have been proposed
to tackle the difficulties of BTS in different technical aspects. However,
existing studies hardly consider how to fuse the multi-modality images in a
reasonable manner. In this paper, we leverage the clinical knowledge of how
radiologists diagnose brain tumors from multiple MRI modalities and propose a
clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS.
Instead of directly concatenating all the modalities, we re-organize the input
modalities by separating them into two groups according to the imaging
principle of MRI. A dual-branch hybrid encoder with the proposed
modality-correlated cross-attention block (MCCA) is designed to extract the
multi-modality image features. The proposed model inherits the strengths from
both Transformer and CNN with the local feature representation ability for
precise lesion boundaries and long-range feature extraction for 3D volumetric
images. To bridge the gap between Transformer and CNN features, we propose a
Trans&CNN Feature Calibration block (TCFC) in the decoder. We compare the
proposed model with five CNN-based models and six transformer-based models on
the BraTS 2021 challenge dataset. Extensive experiments demonstrate that the
proposed model achieves state-of-the-art brain tumor segmentation performance
compared with all the competitors
The Visvalingam algorithm: metrics, measures and heuristics
This paper provides the background necessary for a clear understanding of forthcoming papers relating to the Visvalingam algorithm for line generalisation, for example on the testing and usage of its implementations. It distinguishes the algorithm from implementation-specific issues to explain why it is possible to get inconsistent but equally valid output from different implementations. By tracing relevant developments within the now-disbanded Cartographic Information Systems Research Group (CISRG) of the University of Hull, it explains why a) a partial metric-driven implementation was, and still is, sufficient for many projects but not for others; b) why the Effective Area (EA) is a measure derived from a metric; c) why this measure (EA) may serve as a heuristic indicator for in-line feature segmentation and model-based generalisation; and, d) how metrics may be combined to change the order of point elimination. The issues discussed in this paper also apply to the use of other metrics. It is hoped that the background and guidance provided in this paper will enable others to participate in further research based on the algorithm
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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