Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

膽小鬼

Implicit Neural Representations for Generative Modeling of?Living Cell Shapes images. Deep generative models for cell shape synthesis require a?light-weight and flexible representation of the cell shape. However, commonly used voxel-based representations are unsuitable for high-resolution shape synthesis, and polygon meshes have limitations when modeling topology changes suc

獨白

KEGEL

NerveFormer: A Cross-Sample Aggregation Network for?Corneal Nerve Segmentatione-related diseases and systematic diseases. Existing works mainly use convolutional neural networks to improve the segmentation accuracy, while further improvement is needed to mitigate the nerve discontinuity and noise interference. In this paper, we propose a novel corneal nerve segmentation netwo

VEST

Domain Adaptive Mitochondria Segmentation via?Enforcing Inter-Section Consistencye and time-consuming to collect. Recently, Unsupervised Domain Adaptation (UDA) has been proposed to avoid annotating on target EM volumes by exploiting annotated source EM volumes. However, existing UDA methods for mitochondria segmentation only address the intra-section gap between source and targ

Alveolar-Bone

DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with?Unfolded Hessian for?Stripe Artifactles with decoupled illumination and detection paths. Although the selective excitation scheme of such a microscope provides intrinsic optical sectioning that minimizes out-of-focus fluorescence background and sample photodamage, it is prone to light absorption and scattering effects, which results i

cyanosis

End-to-End Cell Recognition by?Point Annotationpervised methods usually estimate probability density maps for cell recognition. However, in dense cell scenarios, their performance can be limited by pre- and post-processing as it is impossible to find a universal parameter setting. In this paper, we introduce an end-to-end framework that applies

ICLE

偏狂癥

角斗士

DeepMIF: Deep Learning Based Cell Profiling for?Multispectral Immunofluorescence Images with?Graphicwever, complex makeup of markers in the images hinders the accurate quantification of cell phenotypes. We developed DeepMIF, a new deep learning (DL) based tool with a graphical user interface (GUI) to detect and quantify cell phenotypes on M-IF images, and visualize whole slide image (WSI) and cell

起草