@INPROCEEDINGS{Meyer_2019_ISBI,
  author={A. {Meyer} and M. {Rakr} and D. {Schindele} and S. {Blaschke} and M. {Schostak} and A. {Fedorov} and C. {Hansen}},
  booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, 
  title={Towards Patient-Individual PI-Rads v2 Sector Map: Cnn for Automatic Segmentation of Prostatic Zones From T2-Weighted MRI}, 
  year={2019},
  volume={},
  number={},
  pages={696-700},
  abstract={Automatic segmentation of the prostate, its inner and surrounding structures is highly desired for various applications. Several works have been presented for segmentation of anatomical zones of the prostate that are limited to the transition and peripheral zone. Following the spatial division according to the PI-RADS v2 sector map, we present a multi-class segmentation method that additionally targets the anterior fibromuscular stroma and distal prostatic urethra to improve computer-aided detection methods and enable a more precise therapy planning. We propose a multi-class segmentation with an anisotropic convolutional neural network that generates a topologically correct division of the prostate into these four structures. We evaluated our method on a dataset of T2-weighted axial MRI scans (n=98 subjects) and obtained results in the range of inter-rater variability for the majority of the zones.},
  keywords={biological organs;biological tissues;biomedical MRI;image segmentation;image sequences;medical image processing;neural nets;radiation therapy;T2-weighted MRI;anatomical zones;inner surrounding structures;prostatic zones;automatic segmentation;patient-individual PI-RADS;T2-weighted axial MRI scans;topologically correct division;anisotropic convolutional neural network;precise therapy planning;computer-aided detection methods;distal prostatic urethra;anterior fibromuscular stroma;multiclass segmentation method;PI-RADS v2 sector map;spatial division;peripheral zone;Image segmentation;Magnetic resonance imaging;Three-dimensional displays;Training;Image resolution;Medical treatment;Planning;MRI;prostate zone segmentation;PI-RADS v2 sector map;deep convolutional neural networks;therapy planning;computer-aided diagnosis},
  doi={10.1109/ISBI.2019.8759572},
  ISSN={1945-8452},
  month={April},}