AIMC Topic: Image Processing, Computer-Assisted

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Impact of deep learning model uncertainty on manual corrections to MRI-based auto-segmentation in prostate cancer radiotherapy.

Journal of applied clinical medical physics
BACKGROUND: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy. While methods exist to generate voxel-wise uncertainty maps from DL-based auto-segmentation models, these maps are rarely presented to clinicians.

Transforming Skin Quality Evaluation With AI: From Subjective Grading to Data-Driven Precision.

Journal of cosmetic dermatology
BACKGROUND: Skin quality has a significant influence on aesthetic perception, yet its clinical evaluation remains subjective and inconsistent. Traditional assessments, such as visual grading and manual scoring, lack reproducibility and fail to captur...

Current progress of digital twin construction using medical imaging.

Journal of applied clinical medical physics
Medical imaging is fundamental to digital twin technology, enabling patient-specific virtual models of anatomy and physiology. By integrating high-resolution modalities (Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission To...

Time-series X-ray image prediction of dental skeleton treatment progress via neural networks.

Computers in biology and medicine
Accurate prediction of skeletal changes during orthodontic treatment in growing patients remains challenging due to significant individual variability in craniofacial growth and treatment responses. Conventional methods, such as support vector regres...

Deep-Learning-Driven High Spatial Resolution Attenuation Imaging for Ultrasound Tomography (AI-UT).

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasonic attenuation can be used to characterize tissue properties of the human breast. Both quantitative ultrasound (QUS) and ultrasound tomography (USCT) can provide attenuation estimation. However, limitations have been identified for both appro...

Artificial Intelligence for Tumor [F]FDG PET Imaging: Advancements and Future Trends - Part II.

Seminars in nuclear medicine
The integration of artificial intelligence (AI) into [F]FDG PET/CT imaging continues to expand, offering new opportunities for more precise, consistent, and personalized oncologic evaluations. Building on the foundation established in Part I, this se...

Diabetic retinopathy detection from fundus images: A wide survey from grading to segmentation of lesions.

Computers in biology and medicine
Diabetes is one of the most common diseases worldwide and requires accurate diagnosis. Patients with diabetes are often affected by diabetic retinopathy (DR), which can lead to low vision, vision loss, or blindness. Therefore, a robust computer-aided...

Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.

Annals of nuclear medicine
Recent advances in PET image reconstruction have focused on achieving high image quality and quantitative accuracy. Bayesian penalized likelihood (BPL) algorithms, such as Q.Clear and HYPER Iterative that have been integrated into commercial PET syst...

Post-hoc eXplainable AI methods for analyzing medical images of gliomas (- A review for clinical applications).

Computers in biology and medicine
Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) and histopathology images, yet their complexity demands greater transparency in artificial intelligence (AI) systems. This is noticeable when users mu...