Gland Segmentation in Colon Histology Images
by
FIMM Team
Dmitrii Bychkov and Riku Turkki
dmitrii.bychkov@helsinki.fi and riku.turkki@helsinki.fi
University of Helsinki / Institute for Molecular Medicine Finland
Clinical Informatics and Image-based Diagnostics Group
Motivation
We have been seeking for a model which:
Is fairly simple to allow for end-to-end training
Does not require additional pre/post-processing
Learns the features from the data
Network Structure
RGB input
image
7x7
convolution
nxm
@3
2 x 2 max
pooling
nxm
@ 64
11 x 11
convolution
n/2 x 2m/2
@ 64
2 x 2 max
pooling
n/2 x m/2
@ 64
7x7
convolution
n/4 x m/4
@ 64
5x5
convolutional
up-scaling
n/4 x m/4
@ 128
Convolutional Neural Network Architecture
3x3
convolutional
up-scaling
n/2 x m/2
@128
nxm
@1
Inference
RGB input
Decision value map
Binary mask
Training
Scores:
f(x; W)(I,j)
Cost:
1
J=
G
1
∑ max{0, 0.5− f (x;W )i, j } + B ∑ max{0, 0.5+ f (x;W )i, j } + λ R(W )
i∈G
i∈B
Errors over epochs:
Results
Summary
The proposed approach:
can analyse an image in ~ 1 min
achieved near 90% pixel level agreement
But it:
yields somewhat noisy predictions
struggles with high-grade samples
requires fixed-size input