Improving Segmentation and Detection of Lesions in CT Scans Using
  Intensity Distribution Supervision
Seung Yeon Shin Thomas C. Shen Ronald M. Summers
We propose a method to incorporate the intensity information of a target
lesion on CT scans in training segmentation and detection networks. We first
build an intensity-based lesion probability (ILP) function from an intensity
histogram of the target lesion. It is used to compute the probability of being
the lesion for each voxel based on its intensity. Finally, the computed ILP map
of each input CT scan is provided as additional supervision for network
training, which aims to inform the network about possible lesion locations in
terms of intensity values at no additional labeling cost. The method was
applied to improve the segmentation of three different lesion types, namely,
small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness
of the proposed method on a detection task was also investigated. We observed
improvements of 41.3% -> 47.8%, 74.2% -> 76.0%, and 26.4% -> 32.7% in
segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule,
respectively, in terms of per case Dice scores. An improvement of 64.6% ->
75.5% was achieved in detecting kidney tumors in terms of average precision.
The results of different usages of the ILP map and the effect of varied amount
of training data are also presented.