Clinical evaluation of deep learning methods for brain tumor contouring

EasyChair Preprint 2103

Abstract

We aimed to investigate the feasibility of using brain tumor contours generated by a convolutional neural network (CNN) in radiosurgery treatment planning. Ten patients with different diagnosis (four cases of meningioma, two cases of vestibular schwannoma and four cases of multiple brain metastases) were selected from routine clinical practice. Both the axial contrast-enhanced T1-weighted and axial T2 weighted MR images were used for the manual tumors delineation and adjustment of the CNN contours. The axial contrast-enhanced T1-weighted MR images were used to contour the multiple metastases. The tumors were segmented by four experts. We compared the times needed for two contouring techniques: manual delineation of the tumors and a user adjustment of the CNN generated contours of the tumors. The time spent on each task was recorded. To quantify the quality of the CNN generated contours we assessed the similarity between the CNN tumor contour adjusted by the user and the reference contour using the Dice coefficient. To investigate the differences in the Dice scores and to measure time reduction we performed the Sign test and the Wilcoxon test respectively. P-values smaller than 0.05 were assumed to be statistically significant. The usage of the developed algorithm demonstrates significant time reduction and decreased inter-rater variability. The automatically generated contours are a promising tool for standardization of tumor delineation.

Keyphrases: Autosegmentation, Brain Tumor, Convolutional Neural Network, Deep learning method, deep learning, radiosurgery, time reduction

@booklet{EasyChair:2103,
  author    = {Alexandra Dalechina and Egor Krivov and Mikhail Belyaev and Valery Kostjuchenko and Boris Shirokikh and Aleksey Shevtsov and Andrey Golanov and Mikhail Galkin and Amayak Durgaryan and Ivan Osinov},
  title     = {Clinical evaluation of deep learning methods for brain tumor contouring},
  howpublished = {EasyChair Preprint 2103},
  year      = {EasyChair, 2019}}