  Standard segmentation of medical images based on full-supervised
convolutional networks demands accurate dense annotations. Such learning
framework is built on laborious manual annotation with restrict demands for
expertise, leading to insufficient high-quality labels. To overcome such
limitation and exploit massive weakly labeled data, we relaxed the rigid
labeling requirement and developed a semi-supervised learning framework based
on a teacher-student fashion for organ and lesion segmentation with partial
dense-labeled supervision and supplementary loose bounding-box supervision
which are easier to acquire. Observing the geometrical relation of an organ and
its inner lesions in most cases, we propose a hierarchical organ-to-lesion
(O2L) attention module in a teacher segmentor to produce pseudo-labels. Then a
student segmentor is trained with combinations of manual-labeled and
pseudo-labeled annotations. We further proposed a localization branch realized
via an aggregation of high-level features in a deep decoder to predict
locations of organ and lesion, which enriches student segmentor with precise
localization information. We validated each design in our model on LiTS
challenge datasets by ablation study and showed its state-of-the-art
performance compared with recent methods. We show our model is robust to the
quality of bounding box and achieves comparable performance compared with
full-supervised learning methods.
