Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015

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Channel prior convolutional attention for medical image segmentation (2023, methodology): “CNN-based methods, such as U-Net (Ronneberger et al., 2015), have gained widespread popularity in medical image segmentation (Siddique et al.”
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images (2020, methodology): “, 2018) architecture working on sagittal and coronal maximum intensity projections (MIP) of the localised spine region, and finally, 3) Vertebral segmentation, performed by an improved U-Net (Ronneberger et al., 2015; Roy et al., 2018) to segment vertebral patches, extracted at 1mm resolution, ix”
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound (2016, methodology): “Recent works such as [36], [37] performed Hough voting using a CNN.”
Self-derived organ attention for unpaired CT-MRI deep domain adaptation based MRI segmentation (2020, methodology): “Unet : We used a modified Unet (Ronneberger et al 2015) implementation that was composed of series of convolutional blocks.”
Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys (2017, methodology): “The network also incorporates skip connections (similar to a CNN architecture known as U-Net [27] which connect layers at the same resolution and allow the networks to retain spatial information).”