AIMC Topic: Image Processing, Computer-Assisted

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Robotic platform for microinjection into single cells in brain tissue.

EMBO reports
Microinjection into single cells in brain tissue is a powerful technique to study and manipulate neural stem cells. However, such microinjection requires expertise and is a low-throughput process. We developed the "Autoinjector", a robot that utilize...

PET image denoising using unsupervised deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Image quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsup...

Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.

Journal of biophotonics
Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using...

Infrared (IR) thermography as a potential screening modality for carotid artery stenosis.

Computers in biology and medicine
In the present study, an infrared (IR) thermal camera was used to map the temperature of the target skin surface, and the resulting thermal image was evaluated for the presence of carotid artery stenosis (CAS). In the presence of stenosis in the caro...

OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks.

Scientific reports
Organoid cultures are proving to be powerful in vitro models that closely mimic the cellular constituents of their native tissue. Organoids are typically expanded and cultured in a 3D environment using either naturally derived or synthetic extracellu...

Reverse active learning based atrous DenseNet for pathological image classification.

BMC bioinformatics
BACKGROUND: Due to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the hi...

Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks.

IEEE transactions on cybernetics
Deeper and wider convolutional neural networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such overparameterized neural network has received increased attention. A typical pruning algorithm is a three-stage...

Technical Note: PYRO-NN: Python reconstruction operators in neural networks.

Medical physics
PURPOSE: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into ...

Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making.

Surgical infections
There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data for robust clinical decision-support. Limitations of traditional methodologies to handle these data le...

The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction.

Medical physics
PURPOSE: An important challenge for deep learning models is generalizing to new datasets that may be acquired with acquisition protocols different from the training set. It is not always feasible to expand training data to the range encountered in cl...