1,974 research outputs found
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate brain
tumor segmentation. However, it's common to have missing imaging modalities in
clinical practice. Since there exists a strong correlation between multi
modalities, a novel correlation representation block is proposed to specially
discover the latent multi-source correlation. Thanks to the obtained
correlation representation, the segmentation becomes more robust in the case of
missing modalities. The model parameter estimation module first maps the
individual representation produced by each encoder to obtain independent
parameters, then, under these parameters, the correlation expression module
transforms all the individual representations to form a latent multi-source
correlation representation. Finally, the correlation representations across
modalities are fused via the attention mechanism into a shared representation
to emphasize the most important features for segmentation. We evaluate our
model on BraTS 2018 datasets, it outperforms the current state-of-the-art
method and produces robust results when one or more modalities are missing.Comment: 9 pages, 6 figures, accepted by MICCAI 202
Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation
For multi-modal magnetic resonance (MR) brain tumor image segmentation,
current methods usually directly extract the discriminative features from input
images for tumor sub-region category determination and localization. However,
the impact of information aliasing caused by the mutual inclusion of tumor
sub-regions is often ignored. Moreover, existing methods usually do not take
tailored efforts to highlight the single tumor sub-region features. To this
end, a multi-modal MR brain tumor segmentation method with tumor
prototype-driven and multi-expert integration is proposed. It could highlight
the features of each tumor sub-region under the guidance of tumor prototypes.
Specifically, to obtain the prototypes with complete information, we propose a
mutual transmission mechanism to transfer different modal features to each
other to address the issues raised by insufficient information on single-modal
features. Furthermore, we devise a prototype-driven feature representation and
fusion method with the learned prototypes, which implants the prototypes into
tumor features and generates corresponding activation maps. With the activation
maps, the sub-region features consistent with the prototype category can be
highlighted. A key information enhancement and fusion strategy with
multi-expert integration is designed to further improve the segmentation
performance. The strategy can integrate the features from different layers of
the extra feature extraction network and the features highlighted by the
prototypes. Experimental results on three competition brain tumor segmentation
datasets prove the superiority of the proposed method
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
Radiotherapy (RT) combined with cetuximab is the standard treatment for
patients with inoperable head and neck cancers. Segmentation of head and neck
(H&N) tumors is a prerequisite for radiotherapy planning but a time-consuming
process. In recent years, deep convolutional neural networks have become the de
facto standard for automated image segmentation. However, due to the expensive
computational cost associated with enlarging the field of view in DCNNs, their
ability to model long-range dependency is still limited, and this can result in
sub-optimal segmentation performance for objects with background context
spanning over long distances. On the other hand, Transformer models have
demonstrated excellent capabilities in capturing such long-range information in
several semantic segmentation tasks performed on medical images. Inspired by
the recent success of Vision Transformers and advances in multi-modal image
analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin
Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate
cross-modal feature extraction at multiple resolutions.To validate the
effectiveness of the proposed method, we performed experiments on the HECKTOR
2021 challenge dataset and compared it with the nnU-Net (the backbone of the
top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based
methods such as UNETR, and Swin UNETR. The proposed method is experimentally
shown to outperform these comparing methods thanks to the ability of the CMA
module to capture better inter-modality complimentary feature representations
between PET and CT, for the task of head-and-neck tumor segmentation.Comment: 9 pages, 3 figures. Med Phys. 202
SFusion: Self-attention based N-to-One Multimodal Fusion Block
People perceive the world with different senses, such as sight, hearing,
smell, and touch. Processing and fusing information from multiple modalities
enables Artificial Intelligence to understand the world around us more easily.
However, when there are missing modalities, the number of available modalities
is different in diverse situations, which leads to an N-to-One fusion problem.
To solve this problem, we propose a self-attention based fusion block called
SFusion. Different from preset formulations or convolution based methods, the
proposed block automatically learns to fuse available modalities without
synthesizing or zero-padding missing ones. Specifically, the feature
representations extracted from upstream processing model are projected as
tokens and fed into self-attention module to generate latent multimodal
correlations. Then, a modal attention mechanism is introduced to build a shared
representation, which can be applied by the downstream decision model. The
proposed SFusion can be easily integrated into existing multimodal analysis
networks. In this work, we apply SFusion to different backbone networks for
human activity recognition and brain tumor segmentation tasks. Extensive
experimental results show that the SFusion block achieves better performance
than the competing fusion strategies. Our code is available at
https://github.com/scut-cszcl/SFusion.Comment: This paper has been accepted by MICCAI 202
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation
Recent advances in machine learning and prevalence of digital medical images
have opened up an opportunity to address the challenging brain tumor
segmentation (BTS) task by using deep convolutional neural networks. However,
different from the RGB image data that are very widespread, the medical image
data used in brain tumor segmentation are relatively scarce in terms of the
data scale but contain the richer information in terms of the modality
property. To this end, this paper proposes a novel cross-modality deep feature
learning framework to segment brain tumors from the multi-modality MRI data.
The core idea is to mine rich patterns across the multi-modality data to make
up for the insufficient data scale. The proposed cross-modality deep feature
learning framework consists of two learning processes: the cross-modality
feature transition (CMFT) process and the cross-modality feature fusion (CMFF)
process, which aims at learning rich feature representations by transiting
knowledge across different modality data and fusing knowledge from different
modality data, respectively. Comprehensive experiments are conducted on the
BraTS benchmarks, which show that the proposed cross-modality deep feature
learning framework can effectively improve the brain tumor segmentation
performance when compared with the baseline methods and state-of-the-art
methods.Comment: published on Pattern Recognition 202
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
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
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
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