Shown in high resolution images, a morphological feature that can be clearly observed is the bumpy ridges on the inferior aspect of hippocampus, which we refer to as hippocampal dentation. The dentations of the hippocampus in normal individuals vary greatly from highly smooth to highly dentated. The degree of dentation could be an interesting feature which has been shown to be correlated with episodic memory performance and not to be correlated with hippocampal volume. Here we presented a study which quantitatively evaluated the degree of bumpiness under the hippocampi in 552 healthy subjects with the age of mid-20 to 80. Specifically, the principal component analysis (PCA) which is nonlinearly fitted for quantifying the magnitude and the frequency of the hippocampal dentations has been used to identify the major axes of the hippocampus and the dentations under it. Preliminary results have demonstrated that the level of dentations varies between left and right hippocampi in subjects, as well as across different age groups. This can establish an objective and quantitative measurement for such a feature and can be extended for future comparisons between non-clinical and clinical groups.
Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical,
biomedical research, and computer vision fields. Among the multiple observable features spanning multiple
scales in the pathology images, the nuclear morphology is one of the central criteria for diagnosis and grading.
As a result it is also the mostly studied target in image computing. Large amount of research papers have
devoted to the problem of extracting nuclei from digital pathology images, which is the foundation of any
further correlation study. However, the validation and evaluation of nucleus extraction have yet been formulated
rigorously and systematically. Some researches report a human verified segmentation with thousands of nuclei,
whereas a single whole slide image may contain up to million. The main obstacle lies in the difficulty of obtaining
such a large number of validated nuclei, which is essentially an impossible task for pathologist. We propose a
systematic validation and evaluation approach based on large scale image synthesis. This could facilitate a more
quantitatively validated study for current and future histopathology image analysis field.