Automatic Brain Organ Segmentation with 3D Fully Convolutional Neural Network for Radiation Therapy Treatment Planning

3D organ contouring is an essential step in radiation therapy treatment planning for organ dose estimation as well as for optimizing plans to reduce organs-at-risk doses. Manual contouring is time-consuming and its inter-clinician variability adversely affects the outcomes study. Such organs also vary dramatically on sizes - up to two orders of magnitude difference in volumes. In this paper, we present BrainSegNet, a novel 3D fully convolutional neural network (FCNN) based approach for automatic segmentation of brain organs. Brain-SegN et takes a multiple resolution paths approach and uses a weighted loss function to solve the major challenge of the large variability in organ sizes. We evaluated our approach with a dataset of 46 Brain CT image volumes with corresponding expert organ contours as reference. Compared with those of LiviaNet and V-Net, BrainSegNet has a superior performance in segmenting tiny or thin organs, such as chiasm, optic nerves, and cochlea, and outperforms these methods in segmenting large organs as well. BrainSegNet can reduce the manual contouring time of a volume from an hour to less than two minutes, and holds high potential to improve the efficiency of radiation therapy workflow.

[1]  Timothy D. Solberg,et al.  Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs , 2018, Front. Oncol..

[2]  Jose Dolz,et al.  3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study , 2016, NeuroImage.

[3]  David Barber,et al.  Nesterov's accelerated gradient and momentum as approximations to regularised update descent , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[4]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[6]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Yuanyuan Chen,et al.  FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images , 2019, MICCAI.

[8]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[9]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[10]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  Christoph Meinel,et al.  Deep Learning for Medical Image Analysis , 2018, Journal of Pathology Informatics.

[13]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[14]  Hao Chen,et al.  3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes , 2016, MICCAI.

[15]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[16]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[17]  Lubomir M. Hadjiiski,et al.  Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. , 2016, Medical physics.