In this paper, we propose a novel approach to segment tumor and normal regions in
human breast tissues. Cancer is the second most common cause of death in our society;
every eighth woman will be diagnosed with breast cancer in her life. Histological
diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming
analysis and the lack of specialists alike, obtaining a timely diagnosis is often
a difficult process in healthcare institutions, so there is an urgent need for improvement
in diagnostics. To reduce costs and speed up the process, an automated algorithm could
aid routine diagnostics. We propose an area-based annotation approach generalized
by a new rule template to accurately solve high-resolution biological segmentation
tasks in a time-efficient way. These algorithm and implementation rules provide an
alternative solution for pathologists to make decisions as accurate as manually. This
research is based on an individual database from Semmelweis University, containing
291 high-resolution, bright field microscopy breast tumor tissue images. A total of
70% of the 128 x 128-pixel resolution images (206,174 patches) were used for training
a convolutional neural network to learn the features of normal and tumor tissue samples.
The evaluation of the small regions results in high-resolution histopathological image
segmentation; the optimal parameters were calculated on the validation dataset (29
images, 10%), considering the accuracy and time factor as well. The algorithm was
tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level
evaluation within 3 min on average. Besides the quantitative analyses, the system's
accuracy was measured qualitatively by a histopathologist, who confirmed that the
algorithm was also accurate in regions not annotated before.
