Semi-supervised neuron segmentation via reinforced consistency learning
Emerging deep learning-based methods have enabled great progress in automatic neuron
segmentation from Electron Microscopy (EM) volumes. However, the success of existing …
segmentation from Electron Microscopy (EM) volumes. However, the success of existing …
Self-supervised neuron segmentation with multi-agent reinforcement learning
The performance of existing supervised neuron segmentation methods is highly dependent
on the number of accurate annotations, especially when applied to large scale electron …
on the number of accurate annotations, especially when applied to large scale electron …
Large scale image segmentation with structured loss based deep learning for connectome reconstruction
J Funke, F Tschopp, W Grisaitis… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
We present a method combining affinity prediction with region agglomeration, which
improves significantly upon the state of the art of neuron segmentation from electron …
improves significantly upon the state of the art of neuron segmentation from electron …
Residual deconvolutional networks for brain electron microscopy image segmentation
Accurate reconstruction of anatomical connections between neurons in the brain using
electron microscopy (EM) images is considered to be the gold standard for circuit mapping …
electron microscopy (EM) images is considered to be the gold standard for circuit mapping …
UNI-EM: an environment for deep neural network-based automated segmentation of neuronal electron microscopic images
Recently, there has been rapid expansion in the field of micro-connectomics, which targets
the three-dimensional (3D) reconstruction of neuronal networks from stacks of two …
the three-dimensional (3D) reconstruction of neuronal networks from stacks of two …
Co-training with high-confidence pseudo labels for semi-supervised medical image segmentation
Consistency regularization and pseudo labeling-based semi-supervised methods perform
co-training using the pseudo labels from multi-view inputs. However, such co-training …
co-training using the pseudo labels from multi-view inputs. However, such co-training …
Local shape descriptors for neuron segmentation
We present an auxiliary learning task for the problem of neuron segmentation in electron
microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors …
microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors …
Learning multiscale consistency for self-supervised electron microscopy instance segmentation
Electron microscopy (EM) images are notoriously challenging to segment due to their
complex structures and lack of effective annotations. Fortunately, large-scale self-supervised …
complex structures and lack of effective annotations. Fortunately, large-scale self-supervised …
Boosting semi-supervised image segmentation with global and local mutual information regularization
The scarcity of labeled data often impedes the application of deep learning to the
segmentation of medical images. Semi-supervised learning seeks to overcome this …
segmentation of medical images. Semi-supervised learning seeks to overcome this …
Mcf: Mutual correction framework for semi-supervised medical image segmentation
Semi-supervised learning is a promising method for medical image segmentation under
limited annotation. However, the model cognitive bias impairs the segmentation …
limited annotation. However, the model cognitive bias impairs the segmentation …