Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Magnetic Resonance Imaging"
DOI: 10.1002/jmri.28365
Abstract: Deep learning (DL)‐based automatic segmentation models can expedite manual segmentation yet require resource‐intensive fine‐tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine‐tuning is not well characterized. read more here.
Keywords: segmentation algorithms; algorithms automated; generalizability deep; learning segmentation ... See more keywords
Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion
Sign Up to like & getrecommendations! Published in 2024 at "Basic Research in Cardiology"
DOI: 10.1007/s00395-024-01081-x
Abstract: Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically… read more here.
Keywords: deep learning; quantification; learning segmentation; model ... See more keywords
A deep learning segmentation strategy that minimizes the amount of manually annotated images
Sign Up to like & getrecommendations! Published in 2022 at "F1000Research"
DOI: 10.12688/f1000research.52026.2
Abstract: Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets.… read more here.
Keywords: amount; learning segmentation; segmentation; strategy ... See more keywords
Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer
Sign Up to like & getrecommendations! Published in 2023 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2023.868471
Abstract: Purpose The study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP). Methods The DESEP model… read more here.
Keywords: segmentation based; desep; recistv1; learning segmentation ... See more keywords