Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

Osmosis

Transformer-Based Annotation Bias-Aware Medical Image SegmentationRecently, researchers have suggested that such bias is the combination of the annotator preference and stochastic error, which are modeled by convolution blocks located after decoder and pixel-wise independent Gaussian distribution, respectively. It is unlikely that convolution blocks can effectivel

FRAX-tool

進(jìn)取心

A General Stitching Solution for?Whole-Brain 3D Nuclei Instance Segmentation from?Microscopy Imagesteractions within the larger tissue environment in the brain. Despite the significant progress in achieving accurate NIS within small image stacks using cutting-edge machine learning techniques, there has been a lack of effort to extend this approach towards whole-brain NIS from light-sheet microsco

Enzyme

Adult-Like Phase and?Multi-scale Assistance for?Isointense Infant Brain Tissue Segmentationtomatically segment the brain tissues of a 6-month-old infant (isointense phase), even for manual labeling, due to inherent ongoing myelination during the first postnatal year. The intensity contrast between gray matter and white matter is extremely low in isointense MRI data. To resolve this proble

闡釋

carotid-bruit

obtuse

Palpate

Diffusion Kinetic Model for?Breast Cancer Segmentation in?Incomplete DCE-MRIal kinetic characteristics and deep learning to improve segmentation performance. However, the difficulty in accessing complete temporal sequences, especially post-contrast images, hinders segmentation performance, generalization ability and clinical application of existing methods. In this work, we

ECG769

CAS-Net: Cross-View Aligned Segmentation by?Graph Representation of?Kneesple views with stacked 2D slices, ensuring diagnosis accuracy while saving scanning time. However, obtaining fine 3D knee segmentation from multi-view 2D scans is challenging, which is yet necessary for morphological analysis. Moreover, radiologists need to annotate the knee segmentation in multiple

HAUNT

One-Shot Traumatic Brain Segmentation with?Adversarial Training and?Uncertainty Rectificationimited by the lack of annotated data. One-shot segmentation based on learned transformations (OSSLT) has emerged as a powerful tool to overcome the limitations of insufficient training samples, which involves learning spatial and appearance transformations to perform data augmentation, and learning