AIMC Topic: Image Processing, Computer-Assisted

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Evaluation of Deep Learning-Based Approaches to Segment Bowel Air Pockets and Generate Pelvic Attenuation Maps from CAIPIRINHA-Accelerated Dixon MR Images.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Attenuation correction remains a challenge in pelvic PET/MRI. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvic attenuation maps (μ-maps). However, these methods often mis...

Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.

Skeletal radiology
PURPOSE: To develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs.

Low-Light Image Enhancement Based on Multi-Path Interaction.

Sensors (Basel, Switzerland)
Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhan...

Clinically applicable artificial intelligence system for dental diagnosis with CBCT.

Scientific reports
In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinica...

Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT.

Radiation oncology (London, England)
BACKGROUND: This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvi...

Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning.

NeuroImage. Clinical
Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantificat...

Computed Tomography Angiography under Deep Learning in the Treatment of Atherosclerosis with Rapamycin.

Journal of healthcare engineering
The clinical characteristics and vascular computed tomography (CT) imaging characteristics of patients were explored so as to assist clinicians in diagnosing patients with atherosclerosis. 316 patients with atherosclerosis who were hospitalized for e...

Facial recognition accuracy in photographs of Thai neonates with Down syndrome among physicians and the Face2Gene application.

American journal of medical genetics. Part A
Down syndrome (DS) is typically recognizable in those who present with multiple dysmorphism, especially in regard to facial phenotypes. However, as the presentation of DS in neonates is less obvious, a phenotype-based presumptive diagnosis is more ch...

Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.

International journal of computer assisted radiology and surgery
PURPOSE: In cranio-maxillofacial surgery, it is of great clinical significance to segment mandible accurately and automatically from CT images. However, the connected region and blurred boundary in teeth and condyles make the process challenging. At ...

Automatic cell counting from stimulated Raman imaging using deep learning.

PloS one
In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor di...