openTMAS

openTMAS: Open-source Targeted Medical Images Analysis System

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Lung segmentation algorithms

• author: Marcel

• updated on: 05 June 2020

1. U-Net: Convolutional Networks for Biomedical Image Segmentation [May 2015]

• Link to U-Net paper

note: accuracy hits 92% when metadata is combined (I-CNN + Weight + Age + Gender + Height)

2. UNet++: Redesigning Skip Connections to Exploit [Dec 2019]

• Link to UNet++ paper

note: the paper addressed multiscale features in image segmentation well. lung images segmentation is one of the applications in paper.

3. CE-Net: Context Encoder Network for 2D Medical Image Segmentation [March 2019]

• Link to CE-Net paper

note: Paper is released under the tasks of Electron Microscope Images.

4. HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation [April 2018]

• Link to Hyper-Dense-Net paper

note: state-of-the-art segmentation algorithms of Brain MRI images

5. Multi-scale self-guided attention for medical image segmentation [June 2019]

• Link to MS-Dual paper

note: state-of-the-art on Abdominal CT and MRI segmetation.

6. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [June 2016]

• Link to V-Net paper

note: one of the application is on 343 chest CT scans and vnet was took as baseline as following figure

7. UNet 3+: A full-scale connected unet for medical image segmentation [April 2020]

• Link to UNet3+ paper

note: using skip connection improvement over Unet++

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