AIMC Topic: Image Processing, Computer-Assisted

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NuKit: A deep learning platform for fast nucleus segmentation of histopathological images.

Journal of bioinformatics and computational biology
Nucleus segmentation represents the initial step for histopathological image analysis pipelines, and it remains a challenge in many quantitative analysis methods in terms of accuracy and speed. Recently, deep learning nucleus segmentation methods hav...

Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.

Medical physics
BACKGROUND: Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analys...

Clinical application of AI-based PET images in oncological patients.

Seminars in cancer biology
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insuffici...

Deep Learning of Phase-Contrast Images of Cancer Stem Cells Using a Selected Dataset of High Accuracy Value Using Conditional Generative Adversarial Networks.

International journal of molecular sciences
Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs h...

Deep learning for automatic mandible segmentation on dental panoramic x-ray images.

Biomedical physics & engineering express
Many studies in the last decades have correlated mandible bone structure with systemic diseases like osteoporosis. Mandible segmentation, as well as segmentation of other oral structures, is an essential step in studies that correlate oral structures...

PET scatter estimation using deep learning U-Net architecture.

Physics in medicine and biology
Positron emission tomography (PET) image reconstruction needs to be corrected for scatter in order to produce quantitatively accurate images. Scatter correction is traditionally achieved by incorporating an estimated scatter sinogram into the forward...

Deep Learning Body Region Classification of MRI and CT Examinations.

Journal of digital imaging
This study demonstrates the high performance of deep learning in identification of body regions covering the entire human body from magnetic resonance (MR) and computed tomography (CT) axial images across diverse acquisition protocols and modality ma...

A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Preoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time-consuming. Automated methods for segmenting multiple geometrically complex structures ca...

An automatic and accurate deep learning-based neuroimaging pipeline for the neonatal brain.

Pediatric radiology
BACKGROUND: Accurate segmentation of neonatal brain tissues and structures is crucial for studying normal development and diagnosing early neurodevelopmental disorders. However, there is a lack of an end-to-end pipeline for automated segmentation and...

Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data.

European journal of nuclear medicine and molecular imaging
BACKGROUND: For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would...