DeepAI AI Chat
Log In Sign Up

Field Of Interest Proposal for Augmented Mitotic Cell Count: Comparison of two Convolutional Networks

by   Marc Aubreville, et al.

Most tumor grading systems for human as for veterinary histopathology are based upon the absolute count of mitotic figures in a certain reference area of a histology slide. Since time for prognostication is limited in a diagnostic setting, the pathologist will often almost arbitrarily choose a certain field of interest assumed to have the highest mitotic activity. However, as mitotic figures are commonly very sparse on the slide and often have a patchy distribution, this poses a sampling problem which is known to be able to influence the tumor prognostication. On the other hand, automatic detection of mitotic figures can't yet be considered reliable enough for clinical application. In order to aid the work of the human expert and at the same time reduce variance in tumor grading, it is beneficial to assess the whole slide image (WSI) for the highest mitotic activity and use this as a reference region for human counting. For this task, we compare two methods for region of interest proposal, both based on convolutional neural networks (CNN). For both approaches, the CNN performs a segmentation of the WSI to assess mitotic activity. The first method performs a segmentation at the original image resolution, while the second approach performs a segmentation operation at a significantly reduced resolution, cutting down on processing complexity. We evaluate the approach using a dataset of 32 completely annotated whole slide images of canine mast cell tumors, where 22 were used for training of the network and 10 for test. Our results indicate that, while the overall correlation to the ground truth mitotic activity is considerably higher (0.94 vs. 0.83) for the approach based upon the fine resolution network, the field of interest choices are only marginally better. Both approaches propose fields of interest that contain a mitotic count in the upper quartile of respective slides.


page 4

page 6


Augmented Mitotic Cell Count using Field Of Interest Proposal

Histopathological prognostication of neoplasia including most tumor grad...

Field of Interest Prediction for Computer-Aided Mitotic Count

Manual counts of mitotic figures, which are determined in the tumor regi...

3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures

This paper analyzes the use of 3D Convolutional Neural Networks for brai...

A Guided Spatial Transformer Network for Histology Cell Differentiation

Identification and counting of cells and mitotic figures is a standard t...

Truck Axle Detection with Convolutional Neural Networks

Axle count in trucks is important to the classification of vehicles and ...

Large Field and High Resolution: Detecting Needle in Haystack

The growing use of convolutional neural networks (CNN) for a broad range...