… Pathomic features were extracted to characterize the size, morphology, sharpness, and
gray level variation in each segmented nucleus and the heterogeneity patterns of tumor nuclei …

… In this study, a deep learning framework for lung cancer segmentation in whole-slide
histopathology images has been developed. It achieved satisfactory results on the hold-out test …

… To overcome these disadvantages (and because we specifically seek a method for
histology image segmentation), we have developed a localized K-means (LKM) energy to …

… one of image segmentation. Pixel-level labels are required for models to perform segmentation
tasks on histopathological images. … identify object of interests in histopathological images. …

… lung cancer screening in Europe would likely lead to many whole-slide histopathology
images… many medical image analysis methods in CT scans to segregate the lung parenchyma …

… of lung and colon cancers as an alternative to current cancer … a large dataset of lung and
colon histopathology images was … ] utilized image segmentation for the preprocessing of Lung …

… The automatic segmentation of cell nuclei attracted significant interest from the scientific …
analyses based on histopathological images. In this work, for the semantic segmentation phase, …

… histopathological image analysis. We demonstrate its utility and functionality by performing
two case studies on lung cancer and one on colon cancer… complex histopathological analysis …

… and segmentation of nuclei in cytopathology images are … , the segmentation of nuclei on
histopathological images (… are often part of histological structures presenting complex and …

… Due to the complex nature of histopathological images and inherent variable glandular …
-stained histopathology image data for joint gland segmentation and cancer classification tasks. …