Volumetric Medical Image Segmentation
11 papers with code • 1 benchmarks • 1 datasets
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Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images.
In this paper, we test whether this algorithm, which was shown to improve semantic segmentation for 2D RGB images, is able to improve segmentation quality for 3D multi-modal medical images.
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network.
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.
We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume.
In this work, we propose a novel memory-efficient network architecture for 3D high-resolution image segmentation.
For 3D medical image (e. g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly.