Structure-Based Intensity Propagation for 3-D Brain Reconstruction With Multilayer Section Microscopy

Liang, Haoyi; Dabrowska, Natalia; Weller, Daniel S.

doi:10.1109/tmi.2018.2878488

Cited by 5 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Post-registration distortion (Applicable only for registered slices) : We performed registration using [42] on few slices to test Cellcounter's ability (Sup-plementary Fig. 1).…”

Section: (G) Arrowhead)mentioning

confidence: 99%

Cellcounter: a deep learning framework for high-fidelity spatial localization of neurons

Batabyal,

Naik,

Weller

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Many neuroscientific applications require robust and accurate localization of neurons. It is still an unsolved problem because of the enormous variation in intensity, texture, spatial overlap, morphology and background artifacts. In addition, curation of a large dataset containing complete manual annotation of neurons from high-resolution images to train a classifier requires significant time and effort. We present Cellcounter, a deep learning-based model trained on images containing incompletely-annotated neurons with highly-varied morphology and control images containing artifacts and background structures. Leveraging the striking self-learning ability, Cellcounter gradually labels neurons, obviating the need for time-intensive complete annotation. Cellcounter shows its efficacy over the state of the arts in the accurate localization of neurons while significantly reducing false-positive detection in several protocols.

show abstract

“…Post-registration distortion (Applicable only for registered slices) : We performed registration using [42] on few slices to test Cellcounter's ability (Sup-plementary Fig. 1).…”

Section: (G) Arrowhead)mentioning

confidence: 99%

Cellcounter: a deep learning framework for high-fidelity spatial localization of neurons

Batabyal,

Naik,

Weller

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The DG dataset contains 26 high resolution dentate gyrute images, and the image size is from 452 × 942 to 732 × 1336. More details about the DG dataset can be found in our previous work [34].…”

Section: A Datasetsmentioning

confidence: 99%

Enhanced Center Coding for Cell Detection with Convolutional Neural Networks

Liang¹,

Naik²,

Williams³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Cell imaging and analysis are fundamental to biomedical research because cells are the basic functional units of life. Among different cell-related analysis, cell counting and detection are widely used. In this paper, we focus on one common step of learning-based cell counting approaches: coding the raw dot labels into more suitable maps for learning. Two criteria of coding raw dot labels are discussed, and a new coding scheme is proposed in this paper. The two criteria measure how easy it is to train the model with a coding scheme, and how robust the recovered raw dot labels are when predicting. The most compelling advantage of the proposed coding scheme is the ability to distinguish neighboring cells in crowded regions. Cell counting and detection experiments are conducted for five coding schemes on four types of cells and two network architectures. The proposed coding scheme improves the counting accuracy versus the widely-used Gaussian and rectangle kernels up to 12%, and also improves the detection accuracy versus the common proximity coding up to 14%.

show abstract

“…Two methods for structure correction were presented for the brain reconstruction with multilayer tissue sections and they are: Tissue flattening and structure-based intensity propagation. Tissue flattening improves the quality of a reconstructed brain (Liang et al, 2018).…”

Section: Brain Association Graph Brain Network the Connectome Andmentioning

confidence: 99%

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

Wang¹,

Alexander²

2019

Neuroscience International

View full text Add to dashboard Cite

Neuroscience and brain-inspired artificial intelligence are significant research areas. Many countries have launched brain-related projects in which neuroscience and brain-inspired artificial intelligence are major targeted areas to increase national interests and enhance their strength in key areas such as military and homeland security in the competitive global world. Methods, emerging technologies, and progress in neuroscience and brain-inspired artificial intelligence are introduced in this paper that specifically include brain-inspired computing, brain association graph, brain networks, the connectome, brain reconstruction, imaging technologies used for the brain, chips and devices inspired by the human brain, brain-computer interface or brain-machine interfaces, cyborg, neuro-robotics, and quantum robotics. Challenges in some of the topics are also presented and discussed.

show abstract

Structure-Based Intensity Propagation for 3-D Brain Reconstruction With Multilayer Section Microscopy

Cited by 5 publications

References 49 publications

Cellcounter: a deep learning framework for high-fidelity spatial localization of neurons

Cellcounter: a deep learning framework for high-fidelity spatial localization of neurons

Enhanced Center Coding for Cell Detection with Convolutional Neural Networks

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

Contact Info

Product

Resources

About