![](bg7.png)
of the patterns that result from the logical AND op-
eration of I
1
and I
2
. And the binary overlap could be
calculated as:
bo(I
1
,I
2
) =
2 ∗ N
I
1
∩I
2
N
I
1
+ N
I
2
(4)
4.4 Results Analysis
The binary correlation and binary overlap between the
masks from our method and the others are shown in
Table 1. The measures indicate the discrepancies be-
tween our methods compared to the other approaches.
The results show that all the methods work well for
H&E staining. Our method can, however, remove all
the background in the image resulting from a dirty
staining I
2
. For images resulting from a weak staining
I
3
, MobileNet predicts fewer tissues and EfficientNet
could find more tissues. Otsu and MobileNet view
the empty area in I
4
as foreground, EfficientNet and
our method can recognize the empty area and only
consider the tissue as foreground. Due to the empty
area and the white part inside the empty area, the Otsu
missed most tissues in I
4
and I
6
. Our method removes
the small holes predicted by MobileNet in I
5
, I
6
, and
I
7
.
5 CONCLUSIONS
In this paper, we have proposed a solution for the con-
struction of a tissue mask as a pre-processing step
for tissue classification. The masking is based on a
tissue segmentation task, which uses a combination
of mathematical morphology processing on results
from the U-Net architecture. Several experiments of
our method on different types of staining show the
method performs well and leads to better results for
the patching. For the PAS staining, there are still a few
parts of tissue missing. So, here we need to do further
filter and parameter optimization to be as complete
as possible in identifying the tissue parts. Further-
more, we aim to automatically extract the parameters
from the images. This will require further analysis of
a larger number of images.
ACKNOWLEDGEMENTS
This work is partially supported by the Chinese
Scholarship Council (CSC No.202106280008). We
would like to thank the LUMC (Leiden University
Medical Center) to provide the research data.
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