1,280 research outputs found
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
The coronavirus disease (COVID-19) pandemic has led to a devastating effect
on the global public health. Computed Tomography (CT) is an effective tool in
the screening of COVID-19. It is of great importance to rapidly and accurately
segment COVID-19 from CT to help diagnostic and patient monitoring. In this
paper, we propose a U-Net based segmentation network using attention mechanism.
As not all the features extracted from the encoders are useful for
segmentation, we propose to incorporate an attention mechanism including a
spatial and a channel attention, to a U-Net architecture to re-weight the
feature representation spatially and channel-wise to capture rich contextual
relationships for better feature representation. In addition, the focal tversky
loss is introduced to deal with small lesion segmentation. The experiment
results, evaluated on a COVID-19 CT segmentation dataset where 473 CT slices
are available, demonstrate the proposed method can achieve an accurate and
rapid segmentation on COVID-19 segmentation. The method takes only 0.29 second
to segment a single CT slice. The obtained Dice Score, Sensitivity and
Specificity are 83.1%, 86.7% and 99.3%, respectively.Comment: 14 pages, 6 figure
Attention Mechanisms in Medical Image Segmentation: A Survey
Medical image segmentation plays an important role in computer-aided
diagnosis. Attention mechanisms that distinguish important parts from
irrelevant parts have been widely used in medical image segmentation tasks.
This paper systematically reviews the basic principles of attention mechanisms
and their applications in medical image segmentation. First, we review the
basic concepts of attention mechanism and formulation. Second, we surveyed over
300 articles related to medical image segmentation, and divided them into two
groups based on their attention mechanisms, non-Transformer attention and
Transformer attention. In each group, we deeply analyze the attention
mechanisms from three aspects based on the current literature work, i.e., the
principle of the mechanism (what to use), implementation methods (how to use),
and application tasks (where to use). We also thoroughly analyzed the
advantages and limitations of their applications to different tasks. Finally,
we summarize the current state of research and shortcomings in the field, and
discuss the potential challenges in the future, including task specificity,
robustness, standard evaluation, etc. We hope that this review can showcase the
overall research context of traditional and Transformer attention methods,
provide a clear reference for subsequent research, and inspire more advanced
attention research, not only in medical image segmentation, but also in other
image analysis scenarios.Comment: Submitted to Medical Image Analysis, survey paper, 34 pages, over 300
reference
MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images
The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public
health burden and brought profound disaster to humans. For the particularity of
the COVID-19 medical images with blurred boundaries, low contrast and different
sizes of infection sites, some researchers have improved the segmentation
accuracy by adding model complexity. However, this approach has severe
limitations. Increasing the computational complexity and the number of
parameters is unfavorable for model transfer from laboratory to clinic.
Meanwhile, the current COVID-19 infections segmentation DCNN-based methods only
apply to a single modality. To solve the above issues, this paper proposes a
symmetric Encoder-Decoder segmentation framework named MS-DCANet. We introduce
Tokenized MLP block, a novel attention scheme that uses a shift-window
mechanism similar to the Transformer to acquire self-attention and achieve
local-to-global semantic dependency. MS-DCANet also uses several Dual Channel
blocks and a Res-ASPP block to expand the receptive field and extract
multi-scale features. On multi-modality COVID-19 tasks, MS-DCANet achieved
state-of-the-art performance compared with other U-shape models. It can well
trade off the accuracy and complexity. To prove the strong generalization
ability of our proposed model, we apply it to other tasks (ISIC 2018 and BAA)
and achieve satisfactory results
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Recent Progress in Transformer-based Medical Image Analysis
The transformer is primarily used in the field of natural language
processing. Recently, it has been adopted and shows promise in the computer
vision (CV) field. Medical image analysis (MIA), as a critical branch of CV,
also greatly benefits from this state-of-the-art technique. In this review, we
first recap the core component of the transformer, the attention mechanism, and
the detailed structures of the transformer. After that, we depict the recent
progress of the transformer in the field of MIA. We organize the applications
in a sequence of different tasks, including classification, segmentation,
captioning, registration, detection, enhancement, localization, and synthesis.
The mainstream classification and segmentation tasks are further divided into
eleven medical image modalities. A large number of experiments studied in this
review illustrate that the transformer-based method outperforms existing
methods through comparisons with multiple evaluation metrics. Finally, we
discuss the open challenges and future opportunities in this field. This
task-modality review with the latest contents, detailed information, and
comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte
Full-scale Deeply Supervised Attention Network for Segmenting COVID-19 Lesions
Automated delineation of COVID-19 lesions from lung CT scans aids the
diagnosis and prognosis for patients. The asymmetric shapes and positioning of
the infected regions make the task extremely difficult. Capturing information
at multiple scales will assist in deciphering features, at global and local
levels, to encompass lesions of variable size and texture. We introduce the
Full-scale Deeply Supervised Attention Network (FuDSA-Net), for efficient
segmentation of corona-infected lung areas in CT images. The model considers
activation responses from all levels of the encoding path, encompassing
multi-scalar features acquired at different levels of the network. This helps
segment target regions (lesions) of varying shape, size and contrast.
Incorporation of the entire gamut of multi-scalar characteristics into the
novel attention mechanism helps prioritize the selection of activation
responses and locations containing useful information. Determining robust and
discriminatory features along the decoder path is facilitated with deep
supervision. Connections in the decoder arm are remodeled to handle the issue
of vanishing gradient. As observed from the experimental results, FuDSA-Net
surpasses other state-of-the-art architectures; especially, when it comes to
characterizing complicated geometries of the lesions
- …