AIMC Topic: Tomography, X-Ray Computed
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Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks.
PloS one
Dec 3, 2024
In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning,...
Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery.
Hepatology (Baltimore, Md.)
Dec 2, 2024
BACKGROUND AND AIMS: HCC recurrence frequently occurs after curative surgery. Histological microvascular invasion (MVI) predicts recurrence but cannot provide preoperative prognostication, whereas clinical prediction scores have variable performances...
Accurate Airway Tree Segmentation in CT Scans via Anatomy-Aware Multi-Class Segmentation and Topology-Guided Iterative Learning.
IEEE transactions on medical imaging
Dec 2, 2024
Intrathoracic airway segmentation in computed tomography is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease, asthma and lung cancer. Due to the low imaging contrast and noises execrated at periphe...
Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeli...
Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.
Journal of applied clinical medical physics
Dec 1, 2024
PURPOSE: This study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geome...
Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma.
International journal of surgery (London, England)
Dec 1, 2024
BACKGROUND: Extrapancreatic perineural invasion (EPNI) increases the risk of postoperative recurrence in pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop and validate a computed tomography (CT)-based, fully automated preoperative ...
Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study.
International journal of surgery (London, England)
Dec 1, 2024
INTRODUCTION: The postoperative recurrence of gastric cancer (GC) has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of GC is crucial.
External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.
Japanese journal of radiology
Nov 30, 2024
PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the co...
Deep Learning
Lung Neoplasms
Multiple Pulmonary Nodules
Solitary Pulmonary Nodule
Early Detection of Cancer
Radiographic Image Interpretation, Computer-Assisted
Japan
Radiation Dosage
Humans
Sensitivity and Specificity
Female
Retrospective Studies
Male
Aged
Tomography, X-Ray Computed
Middle Aged
Aged, 80 and over
Lung
BMSMM-Net: A Bone Metastasis Segmentation Framework Based on Mamba and Multiperspective Extraction.
Academic radiology
Nov 30, 2024
RATIONALE AND OBJECTIVES: Metastatic bone tumors significantly reduce patients' quality of life and expedite cancer spread. Traditional diagnostic methods rely on time-consuming manual annotations by radiologists, which are prone to subjectivity. Emp...
Predicting sinonasal inverted papilloma attachment using machine learning: Current lessons and future directions.
American journal of otolaryngology
Nov 30, 2024
BACKGROUND: Hyperostosis is a common radiographic feature of inverted papilloma (IP) tumor origin on computed tomography (CT). Herein, we developed a machine learning (ML) model capable of analyzing CT images and identifying IP attachment sites.
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