AIMC Topic: Image Processing, Computer-Assisted

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Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highligh...

Comparative performance assessment of deep learning based image steganography techniques.

Scientific reports
Increasing data infringement while transmission and storage have become an apprehension for the data owners. Even the digital images transmitted over the network or stored at servers are prone to unauthorized access. However, several image steganogra...

A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.

Medical physics
BACKGROUND: Most of existing deep learning research in medical image analysis is focused on networks with stronger performance. These networks have achieved success, while their architectures are complex and even contain massive parameters ranging fr...

An automatic extraction method on medical feature points based on PointNet++ for robot-assisted knee arthroplasty.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Image registration is a crucial technology in robot-assisted knee arthroplasty, which provides real-time patient information by registering the pre-operative image data with data acquired during the operation. The existing registration me...

Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.

Nature communications
Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanne...

A deep learning approach to real-time volumetric measurements without image reconstruction for cardiovascular magnetic resonance.

Physiological measurement
Cardiovascular magnetic resonance (CMR) can measure ventricular volumes for the quantitative assessment of cardiac function in clinical cardiology. Conventionally, CMR volumetric measurements require image reconstruction and segmentation. There are l...

MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation.

Computational and mathematical methods in medicine
The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capt...

A VS ultrasound diagnostic system with kidney image evaluation functions.

International journal of computer assisted radiology and surgery
PURPOSE: An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe posi...

Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.

European radiology
OBJECTIVES: To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness...