AIMC Topic: Image Processing, Computer-Assisted

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Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search.

Computers in biology and medicine
Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification of intersection and bifurcation points is quite challenging but impo...

Utilization of an attentive map to preserve anatomical features for training convolutional neural-network-based low-dose CT denoiser.

Medical physics
BACKGROUND: The purpose of a convolutional neural network (CNN)-based denoiser is to increase the diagnostic accuracy of low-dose computed tomography (LDCT) imaging. To increase diagnostic accuracy, there is a need for a method that reflects the feat...

A Deep Learning Approach for Automated Bone Removal from Computed Tomography Angiography of the Brain.

Journal of digital imaging
Advanced visualization techniques such as maximum intensity projection (MIP) and volume rendering (VR) are useful for evaluating neurovascular anatomy on CT angiography (CTA) of the brain; however, interference from surrounding osseous anatomy is com...

CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation.

Medical image analysis
The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI) provides a valuable quantitative tool for the analysis of the morphology and course of individual CNs. Tractography-based approaches can describe and...

Reducing both radiation and contrast doses for overweight patients in coronary CT angiography with 80-kVp and deep learning image reconstruction.

European journal of radiology
PURPOSE: To investigate the use of an 80-kVp tube voltage combined with a deep learning image reconstruction (DLIR) algorithm in coronary CT angiography (CCTA) for overweight patients to reduce radiation and contrast doses in comparison with the 120-...

Deep learning based direct segmentation assisted by deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy.

Physics in medicine and biology
Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians. However, deep learning (DL)-based direct segmentation of CBCT images is a challenging task, mainly due to the poor ima...

HCTNet: A hybrid CNN-transformer network for breast ultrasound image segmentation.

Computers in biology and medicine
Automatic breast ultrasound image segmentation helps radiologists to improve the accuracy of breast cancer diagnosis. In recent years, the convolutional neural networks (CNNs) have achieved great success in medical image analysis. However, it exhibit...

Recent advances in plant disease severity assessment using convolutional neural networks.

Scientific reports
In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of diseas...

Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization.

Scientific reports
The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnan...

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on...