AIMC Topic: Image Processing, Computer-Assisted

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Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics.

Surgical pathology clinics
As machine learning (ML) solutions for genitourinary pathology image analysis are fostered by a progressively digitized laboratory landscape, these integrable modalities usher in a revolution in histopathological diagnosis. As technology advances, li...

[Automatic Delineation of Clinical Target Volume and Organ at Risk by Deep Learning for Prostate Cancer Adaptive Radiotherapy].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Adaptive radiotherapy can modify the treatment plan online based on the clinical target volume (CTV) and organ at risk (OAR) contours on the cone-beam CT (CBCT) before treatment, improving the accuracy of radiotherapy. However, manual delineation of ...

Improving and evaluating deep learning models of cellular organization.

Bioinformatics (Oxford, England)
MOTIVATION: Cells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both tradi...

Anti-noise computational imaging using unsupervised deep learning.

Optics express
Computational imaging enables spatial information retrieval of objects with the use of single-pixel detectors. By combining measurements and computational methods, it is possible to reconstruct images in a variety of situations that are challenging o...

[Cascaded multi-level medical image registration method based on transformer].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anato...

Fourier ptychographic microscopy with untrained deep neural network priors.

Optics express
We propose a physics-assisted deep neural network scheme in Fourier ptychographic microscopy (FPM) using untrained deep neural network priors (FPMUP) to achieve a high-resolution image reconstruction from multiple low-resolution images. Unlike the tr...

Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction "TrueFidelity" in children with congenital heart disease.

Medicine
BACKGROUND: Several recent studies have reported that deep learning reconstruction "TrueFidelity" (TF) improves computed tomography (CT) image quality. However, no study has compared adaptive statistical repeated reconstruction (ASIR-V) using TF in p...

Dynamic single-photon 3D imaging with a sparsity-based neural network.

Optics express
Deep learning is emerging as an important tool for single-photon light detection and ranging (LiDAR) with high photon efficiency and image reconstruction quality. Nevertheless, the existing deep learning methods still suffer from high memory footprin...

Edge feature extraction-based dual CNN for LDCT denoising.

Journal of the Optical Society of America. A, Optics, image science, and vision
In low-dose computed tomography (LDCT) denoising tasks, it is often difficult to balance edge/detail preservation and noise/artifact reduction. To solve this problem, we propose a dual convolutional neural network (CNN) based on edge feature extracti...

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.

Journal of biomedical optics
SIGNIFICANCE: Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imagin...