BESNet: Boundary-enhanced segmentation of cells in histopathological images

TY - CHAP

T1 - BESNet

T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

AU - Oda, Hirohisa

AU - Roth, Holger R.

AU - Chiba, Kosuke

AU - Sokolić, Jure

AU - Kitasaka, Takayuki

AU - Oda, Masahiro

AU - Hinoki, Akinari

AU - Uchida, Hiroo

AU - Schnabel, Julia A.

AU - Mori, Kensaku

PY - 2018/1/1

Y1 - 2018/1/1

N2 - We propose a novel deep learning method called Boundary-Enhanced Segmentation Network (BESNet) for the detection and semantic segmentation of cells on histopathological images. The semantic segmentation of small regions using fully convolutional networks typically suffers from inaccuracies around the boundaries of small structures, like cells, because the probabilities often become blurred. In this work, we propose a new network structure that encodes input images to feature maps similar to U-net but utilizes two decoding paths that restore the original image resolution. One decoding path enhances the boundaries of cells, which can be used to improve the quality of the entire cell segmentation achieved in the other decoding path. We explore two strategies for enhancing the boundaries of cells: (1) skip connections of feature maps, and (2) adaptive weighting of loss functions. In (1), the feature maps from the boundary decoding path are concatenated with the decoding path for entire cell segmentation. In (2), an adaptive weighting of the loss for entire cell segmentation is performed when boundaries are not enhanced strongly, because detecting such parts is difficult. The detection rate of ganglion cells was 80.0% with 1.0 false positives per histopathology slice. The mean Dice index representing segmentation accuracy was 74.0%. BESNet produced a similar detection performance and higher segmentation accuracy than comparable U-net architectures without our modifications.

AB - We propose a novel deep learning method called Boundary-Enhanced Segmentation Network (BESNet) for the detection and semantic segmentation of cells on histopathological images. The semantic segmentation of small regions using fully convolutional networks typically suffers from inaccuracies around the boundaries of small structures, like cells, because the probabilities often become blurred. In this work, we propose a new network structure that encodes input images to feature maps similar to U-net but utilizes two decoding paths that restore the original image resolution. One decoding path enhances the boundaries of cells, which can be used to improve the quality of the entire cell segmentation achieved in the other decoding path. We explore two strategies for enhancing the boundaries of cells: (1) skip connections of feature maps, and (2) adaptive weighting of loss functions. In (1), the feature maps from the boundary decoding path are concatenated with the decoding path for entire cell segmentation. In (2), an adaptive weighting of the loss for entire cell segmentation is performed when boundaries are not enhanced strongly, because detecting such parts is difficult. The detection rate of ganglion cells was 80.0% with 1.0 false positives per histopathology slice. The mean Dice index representing segmentation accuracy was 74.0%. BESNet produced a similar detection performance and higher segmentation accuracy than comparable U-net architectures without our modifications.

UR - http://www.scopus.com/inward/record.url?scp=85054067193&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-00934-2_26

DO - 10.1007/978-3-030-00934-2_26

M3 - Conference paper

AN - SCOPUS:85054067193

SN - 9783030009335

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 228

EP - 236

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings

PB - Springer Verlag

Y2 - 16 September 2018 through 20 September 2018

ER -