  Cancer segmentation in whole-slide images is a fundamental step for viable
tumour burden estimation, which is of great value for cancer assessment.
However, factors like vague boundaries or small regions dissociated from viable
tumour areas make it a challenging task. Considering the usefulness of
multi-scale features in various vision-related tasks, we present a
structure-aware scale-adaptive feature selection method for efficient and
accurate cancer segmentation. Based on a segmentation network with a popular
encoder-decoder architecture, a scale-adaptive module is proposed for selecting
more robust features to represent the vague, non-rigid boundaries. Furthermore,
a structural similarity metric is proposed for better tissue structure
awareness to deal with small region segmentation. In addition, advanced designs
including several attention mechanisms and the selective-kernel convolutions
are applied to the baseline network for comparative study purposes. Extensive
experimental results show that the proposed structure-aware scale-adaptive
networks achieve outstanding performance on liver cancer segmentation when
compared to top ten submitted results in the challenge of PAIP 2019. Further
evaluation on colorectal cancer segmentation shows that the scale-adaptive
module improves the baseline network or outperforms the other excellent designs
of attention mechanisms when considering the tradeoff between efficiency and
accuracy.
