630 research outputs found
Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation
Recently 3D volumetric organ segmentation attracts much research interest in
medical image analysis due to its significance in computer aided diagnosis.
This paper aims to address the pancreas segmentation task in 3D computed
tomography volumes. We propose a novel end-to-end network, Globally Guided
Progressive Fusion Network, as an effective and efficient solution to
volumetric segmentation, which involves both global features and complicated 3D
geometric information. A progressive fusion network is devised to extract 3D
information from a moderate number of neighboring slices and predict a
probability map for the segmentation of each slice. An independent branch for
excavating global features from downsampled slices is further integrated into
the network. Extensive experimental results demonstrate that our method
achieves state-of-the-art performance on two pancreas datasets.Comment: MICCAI201
APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation
In 3D medical image segmentation, small targets segmentation is crucial for
diagnosis but still faces challenges. In this paper, we propose the Axis
Projection Attention UNet, named APAUNet, for 3D medical image segmentation,
especially for small targets. Considering the large proportion of the
background in the 3D feature space, we introduce a projection strategy to
project the 3D features into three orthogonal 2D planes to capture the
contextual attention from different views. In this way, we can filter out the
redundant feature information and mitigate the loss of critical information for
small lesions in 3D scans. Then we utilize a dimension hybridization strategy
to fuse the 3D features with attention from different axes and merge them by a
weighted summation to adaptively learn the importance of different
perspectives. Finally, in the APA Decoder, we concatenate both high and low
resolution features in the 2D projection process, thereby obtaining more
precise multi-scale information, which is vital for small lesion segmentation.
Quantitative and qualitative experimental results on two public datasets (BTCV
and MSD) demonstrate that our proposed APAUNet outperforms the other methods.
Concretely, our APAUNet achieves an average dice score of 87.84 on BTCV, 84.48
on MSD-Liver and 69.13 on MSD-Pancreas, and significantly surpass the previous
SOTA methods on small targets.Comment: Accepted by ACCV202
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
Recently, the advent of vision Transformer (ViT) has brought substantial
advancements in 3D dataset benchmarks, particularly in 3D volumetric medical
image segmentation (Vol-MedSeg). Concurrently, multi-layer perceptron (MLP)
network has regained popularity among researchers due to their comparable
results to ViT, albeit with the exclusion of the resource-intensive
self-attention module. In this work, we propose a novel permutable hybrid
network for Vol-MedSeg, named PHNet, which capitalizes on the strengths of both
convolution neural networks (CNNs) and MLP. PHNet addresses the intrinsic
isotropy problem of 3D volumetric data by employing a combination of 2D and 3D
CNNs to extract local features. Besides, we propose an efficient multi-layer
permute perceptron (MLPP) module that captures long-range dependence while
preserving positional information. This is achieved through an axis
decomposition operation that permutes the input tensor along different axes,
thereby enabling the separate encoding of the positional information.
Furthermore, MLPP tackles the resolution sensitivity issue of MLP in Vol-MedSeg
with a token segmentation operation, which divides the feature into smaller
tokens and processes them individually. Extensive experimental results validate
that PHNet outperforms the state-of-the-art methods with lower computational
costs on the widely-used yet challenging COVID-19-20 and Synapse benchmarks.
The ablation study also demonstrates the effectiveness of PHNet in harnessing
the strengths of both CNNs and MLP
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