AIMC Topic: Image Processing, Computer-Assisted

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3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.

eLife
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We develope...

A deep learning segmentation strategy that minimizes the amount of manually annotated images.

F1000Research
Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance ...

SSCA-Net: Simultaneous Self- and Channel-Attention Neural Network for Multiscale Structure-Preserving Vessel Segmentation.

BioMed research international
Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to the complicated geometrical and topological structures of human vessels. Unlike existing rule- and conventional learning-based techniques, which hardl...

Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous segmentation and registration.

NeuroImage
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear regi...

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification.

PLoS computational biology
High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in...

U-net model for brain extraction: Trained on humans for transfer to non-human primates.

NeuroImage
Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed...

Transfer learning in deep neural network-based receiver coil sensitivity map estimation.

Magma (New York, N.Y.)
INTRODUCTION: The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depend...

Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.

European journal of nuclear medicine and molecular imaging
PURPOSE: In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter image...

PyDiNet: Pyramid Dilated Network for medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Medical image segmentation is an important step in many generic applications such as population analysis and, more accessible, can be made into a crucial tool in diagnosis and treatment planning. Previous approaches are based on two main architecture...

Image Quality Enhancement Using a Deep Neural Network for Plane Wave Medical Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Plane wave imaging (PWI), a typical ultrafast medical ultrasound imaging mode, adopts single plane wave emission without focusing to achieve a high frame rate. However, the imaging quality is severely degraded in comparison with the commonly used foc...