AIMC Topic: Image Processing, Computer-Assisted

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Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation.

IEEE transactions on bio-medical engineering
Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times larger than that of the ...

Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm.

IEEE transactions on bio-medical engineering
OBJECTIVE: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in exces...

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation.

IEEE transactions on bio-medical engineering
OBJECTIVE: The scarcity of high-quality annotated data is omnipresent in machine learning. Especially in biomedical segmentation applications, experts need to spend a lot of their time into annotating due to the complexity. Hence, methods to reduce s...

Deep Learning Ultrasound Computed Tomography Under Sparse Sampling.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound computed tomography (USCT) is a fast-emerging imaging modality that is expected to help detect and characterize breast tumors by quantifying the distribution of the speed of sound (SOS) and acoustic attenuation in breast tissue. High-quali...

The Big Bang of Deep Learning in Ultrasound-Guided Surgery: A Review.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound (US) imaging is a paramount modality in many image-guided surgeries and percutaneous interventions, thanks to its high portability, temporal resolution, and cost-efficiency. However, due to its imaging principles, the US is often noisy and...

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy.

Physics in medicine and biology
Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No commercial models exist for OAR contouring based on brain magnetic resonance imaging (MRI...

Automatic detection and segmentation of chorda tympani under microscopic vision in otosclerosis patients via convolutional neural networks.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Artificial intelligence (AI) techniques, especially deep learning (DL) techniques, have shown promising results for various computer vision tasks in the field of surgery. However, AI-guided navigation during microscopic surgery for real-t...

Recent Advances of Biosensors for Detection of Multiple Antibiotics.

Biosensors
The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of th...

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T.

NMR in biomedicine
At ultrahigh field strengths images of the body are hampered by B -field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a "bias field" to the ideal image. Current bias field correction m...

LMU-Net: lightweight U-shaped network for medical image segmentation.

Medical & biological engineering & computing
Deep learning technology has been employed for precise medical image segmentation in recent years. However, due to the limited available datasets and real-time processing requirement, the inherently complicated structure of deep learning models restr...