Publications by authors named "Sergei V Fotin"
Article Synopsis
- A study was conducted to assess how artificial intelligence (AI) can reduce reading time for digital breast tomosynthesis (DBT) while maintaining or improving accuracy in detecting lesions.
- The introduction of a deep learning AI system led to significant improvements: radiologists' detection performance (measured by AUC) increased from 0.795 to 0.852, and reading time decreased by about 52.7%.
- Overall, the use of AI not only enhanced sensitivity and specificity for detecting malignant lesions but also lowered the recall rate for non-cancer findings, indicating it could be a valuable tool in breast imaging.
Objective: The objective of our study was to compare calculated prostate volumes derived from tridimensional MR measurements (ellipsoid formula), manual segmentation, and a fully automated segmentation system as validated by actual prostatectomy specimens.
Materials And Methods: Ninety-eight consecutive patients (median age, 60.6 years; median prostate-specific antigen [PSA] value, 6.
Automated nodule location and size estimation using a multi-scale Laplacian of Gaussian filtering approach.
Annu Int Conf IEEE Eng Med Biol Soc
April 2010
Estimation of nodule location and size is an important pre-processing step in some nodule segmentation algorithms to determine the size and location of the region of interest. Ideally, such estimation methods will consistently find the same nodule location regardless of where the the seed point (provided either manually or by a nodule detection algorithm) is placed relative to the "true" center of the nodule, and the size should be a reasonable estimate of the true nodule size. We developed a method that estimates nodule location and size using multi-scale Laplacian of Gaussian (LoG) filtering.
View Article and Find Full Text PDF