AIMC Topic: Image Processing, Computer-Assisted

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Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.

Scientific reports
This study compares various preprocessing techniques for hyperspectral deep learning-based cancer diagnostics. The study considers different spectrum scaling and noise reduction options across spatial and spectral axes of hyperspectral datacubes, as ...

A 3D multi-task network for the automatic segmentation of CT images featuring hip osteoarthritis.

Biomedical physics & engineering express
Total hip arthroplasty (THA) is the primary treatment for end-stage hip osteoarthritis, with successful outcomes depending on precise preoperative planning that requires accurate segmentation and reconstruction of periarticular bone of the hip joint....

Image aesthetic quality assessment: A method based on deep convolutional capsule network.

PloS one
Image aesthetics assessment (IAA) has become a hot research area in recent years due to its extensive application potential. However, existing IAA methods often overlook the importance of spatial information in evaluating image aesthetics. To address...

Synthetizing SWI from 3T to 7T by generative diffusion network for deep medullary veins visualization.

NeuroImage
Ultrahigh-field susceptibility-weighted imaging (SWI) provides excellent tissue contrast and anatomical details of brain. However, ultrahigh-field magnetic resonance (MR) scanner often expensive and provides uncomfortable noise experience for patient...

MRI annotation using an inversion-based preprocessing for CT model adaptation.

European radiology experimental
BACKGROUND: Annotating new classes in MRI images is time-consuming. Refining presegmented structures can accelerate this process. Many target classes lacking in MRI are supported by computed tomography (CT) models, but translating MRI to synthetic CT...

PWLS-SOM: alternative PWLS reconstruction for limited-view CT by strategic optimization of a deep learning model.

Physics in medicine and biology
While deep learning (DL) methods have exhibited promising results in mitigating streaking artifacts caused by limited-view computed tomography (CT), their generalization to practical applications remains challenging. To address this challenge, we aim...

A Compound-Eye-Inspired Multi-Scale Neural Architecture with Integrated Attention Mechanisms.

International journal of neural systems
In the context of neural system structure modeling and complex visual tasks, the effective integration of multi-scale features and contextual information is critical for enhancing model performance. This paper proposes a biologically inspired hybrid ...

Multi-scale error-driven dense residual network for image super-resolution reconstruction.

PloS one
Image super-resolution reconstructs high-resolution images from low-resolution inputs. However, current single-image super-resolution techniques often struggle to capture multi-scale information and extract high-frequency details, which compromises r...

DBCM-net:dual backbone cascaded multi-convolutional segmentation network for medical image segmentation.

Biomedical physics & engineering express
Medical image segmentation plays a vital role in diagnosis, treatment planning, and disease monitoring. However, endoscopic and dermoscopic images often exhibit blurred boundaries and low contrast, presenting a significant challenge for precise segme...

Disentangled deep learning method for interior tomographic reconstruction of low-dose x-ray CT.

Physics in medicine and biology
. Low-dose interior tomography integrates low-dose CT (LDCT) with region-of-interest (ROI) imaging which finds wide application in radiation dose reduction and high-resolution imaging. However, the combined effects of noise and data truncation pose g...