Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI
  using Deep Learning
Douwe J. Spaanderman Martijn P. A. Starmans Gonnie C. M. van Erp David F. Hanff Judith H. Sluijter Anne-Rose W. Schut Geert J. L. H. van Leenders Cornelis Verhoef Dirk J. Grunhagen Wiro J. Niessen Jacob J. Visser Stefan Klein
Segmentations are crucial in medical imaging to obtain morphological,
volumetric, and radiomics biomarkers. Manual segmentation is accurate but not
feasible in the radiologist's clinical workflow, while automatic segmentation
generally obtains sub-par performance. We therefore developed a minimally
interactive deep learning-based segmentation method for soft-tissue tumors
(STTs) on CT and MRI. The method requires the user to click six points near the
tumor's extreme boundaries. These six points are transformed into a distance
map and serve, with the image, as input for a Convolutional Neural Network. For
training and validation, a multicenter dataset containing 514 patients and nine
STT types in seven anatomical locations was used, resulting in a Dice
Similarity Coefficient (DSC) of 0.85$\pm$0.11 (mean $\pm$ standard deviation
(SD)) for CT and 0.84$\pm$0.12 for T1-weighted MRI, when compared to manual
segmentations made by expert radiologists. Next, the method was externally
validated on a dataset including five unseen STT phenotypes in extremities,
achieving 0.81$\pm$0.08 for CT, 0.84$\pm$0.09 for T1-weighted MRI, and
0.88\pm0.08 for previously unseen T2-weighted fat-saturated (FS) MRI. In
conclusion, our minimally interactive segmentation method effectively segments
different types of STTs on CT and MRI, with robust generalization to previously
unseen phenotypes and imaging modalities.