Segmentation of thoracic organ is a challenging task as demonstrated by the SegTHOR challenge. In this paper, we present an efficient yet simple framework for automatic thoracic organ segmentation. Two steps are included: first, we designed a simple network to define the ROI of the input volume. Second, we propose a network based on the encoder and decoder model. Three orthogonal views are fed into the network, respectively. To do the final segmentation, we used ensemble by majority voting. The experiments show that our approach is comparable with other segmentation networks.
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2019 Jan 1|
|Event||2019 SegTHOR Challenge: Segmentation of THoracic Organs at Risk in CT Images, SegTHOR 2019 - Venice, Italy|
Duration: 2019 Apr 10 → …
All Science Journal Classification (ASJC) codes
- Computer Science(all)