Weakly Supervised Segmentation of Vertebral Bodies with Iterative
  Slice-propagation
Shiqi Peng Bolin Lai Guangyu Yao Xiaoyun Zhang Ya Zhang Yan-Feng Wang Hui Zhao
Vertebral body (VB) segmentation is an important preliminary step towards
medical visual diagnosis for spinal diseases. However, most previous works
require pixel/voxel-wise strong supervisions, which is expensive, tedious and
time-consuming for experts to annotate. In this paper, we propose a Weakly
supervised Iterative Spinal Segmentation (WISS) method leveraging only four
corner landmark weak labels on a single sagittal slice to achieve automatic
volumetric segmentation from CT images for VBs. WISS first segments VBs on an
annotated sagittal slice in an iterative self-training manner. This
self-training method alternates between training and refining labels in the
training set. Then WISS proceeds to segment the whole VBs slice by slice with a
slice-propagation method to obtain volumetric segmentations. We evaluate the
performance of WISS on a private spinal metastases CT dataset and the public
lumbar CT dataset. On the first dataset, WISS achieves distinct improvements
with regard to two different backbones. For the second dataset, WISS achieves
dice coefficients of $91.7\%$ and $83.7\%$ for mid-sagittal slices and 3D CT
volumes, respectively, saving a lot of labeling costs and only sacrificing a
little segmentation performance.