AIMC Topic: Image Processing, Computer-Assisted

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Early pigment spot segmentation and classification from iris cellular image analysis with explainable deep learning and multiclass support vector machine.

Biochemistry and cell biology = Biochimie et biologie cellulaire
Globally, retinal disorders impact thousands of individuals. Early diagnosis and treatment of these anomalies might halt their development and prevent many people from developing preventable blindness. Iris spot segmentation is critical due to acquir...

"A net for everyone": fully personalized and unsupervised neural networks trained with longitudinal data from a single patient.

BMC medical imaging
BACKGROUND: With the rise in importance of personalized medicine and deep learning, we combine the two to create personalized neural networks. The aim of the study is to show a proof of concept that data from just one patient can be used to train dee...

Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography.

Academic radiology
RATIONALE AND OBJECTIVES: Pericardial fat (PF)-the thoracic visceral fat surrounding the heart-promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. To evaluate PF, we generated pericardial fat count i...

Deep learning assisted sparse array ultrasound imaging.

PloS one
This study aims to restore grating lobe artifacts and improve the image resolution of sparse array ultrasonography via a deep learning predictive model. A deep learning assisted sparse array was developed using only 64 or 16 channels out of the 128 c...

Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning.

Ultrasonics
Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast t...

Deep learning-based ultrasound auto-segmentation of the prostate with brachytherapy implanted needles.

Medical physics
BACKGROUND: Accurate segmentation of the clinical target volume (CTV) corresponding to the prostate with or without proximal seminal vesicles is required on transrectal ultrasound (TRUS) images during prostate brachytherapy procedures. Implanted need...

Deep learning in mesoscale brain image analysis: A review.

Computers in biology and medicine
Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, th...

EPT-Net: Edge Perception Transformer for 3D Medical Image Segmentation.

IEEE transactions on medical imaging
The convolutional neural network has achieved remarkable results in most medical image seg- mentation applications. However, the intrinsic locality of convolution operation has limitations in modeling the long-range dependency. Although the Transform...

FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation.

IEEE transactions on neural networks and learning systems
The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has es...

SpheroScan: a user-friendly deep learning tool for spheroid image analysis.

GigaScience
BACKGROUND: In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has...