Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans.
IEEE Trans Med Imaging. 2019 Jul 23. doi: 10.1109/TMI.2019.2930679. [Epub ahead of print]
Xie L, Yu Q, Wang Y, Zhou Y, Fishman EK, Yuille AL.
We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland) and neoplasms (e.g., pancreatic cyst), from abdominal CT scans. This is a challenging task in two aspects. First, some organs (e.g., the pancreas), are highly variable in both anatomy and geometry, and thus very difficult to depict. Second, the neoplasms often vary a lot in its size, shape, as well as its location within the organ. Third, the targets (organs and neoplasms) can be considerably small compared to the human body, and so standard deep networks for segmentation are often less sensitive to these targets and thus predict less accurately especially around their boundaries. In this paper, we present an end-to-end framework named Recurrent Saliency Transformation Network (RSTN) for segmenting tiny and/or variable targets. RSTN is a coarse-to-fine approach, which uses prediction from the first (coarse) stage to shrink the input region for the second (fine) stage. A saliency transformation module is inserted between these two stages, so that (i) the coarse-scaled segmentation mask can be transferred as spatial weights and applied to the fine stage; and (ii) the gradients can be back-propagated from the loss layer to the entire network, so that the two stages are optimized in a joint manner. In the testing stage, we perform segmentation iteratively to improve accuracy. In this extended journal paper, we allow a gradual optimization to improve the stability of RSTN, and introduce a hierarchical version named H-RSTN to segment tiny and variable neoplasms such as pancreatic cysts. Experiments are performed on several CT datasets, including a public pancreas segmentation dataset, our own multi-organ dataset, and a cystic pancreas dataset. In all these cases, RSTN outperforms the baseline (a stage-wise coarse-to-fine approach) significantly. Confirmed by the radiologists in our team, these promising segmentation results can help early diagnosis of pancreatic cancer. The code and pre-trained models of our project were made available at https://github.com/198808xc/OrganSegRSTN.
Read Full Article Here: https://doi.org/10.1109/TMI.2019.2930679