A Web-based Brain Metastases Segmentation and Labeling Platform for Stereotactic Radiosurgery.

PURPOSE Stereotactic radiosurgery (SRS) serves as a standard of care of brain metastases (BMs), however, the BMs delineation in the SRS workflow can be time-consuming. The manual contouring can be a pronounced bottleneck in multiple BMs, but there is a lack of tools for automatic delineation and quantitative evaluation. In this study, based on our previous developed deep learning-based segmentation algorithms, we developed a web-based automated BMs segmentation and labeling platform to assist the SRS clinical workflow. METHOD This platform is developed based on the Django framework, including a web client and a back-end server. The web client enables interactions as database access, data import, image viewing. The server performs the segmentation and labeling tasks including: (1) skull stripping; (2) deep learning-based BMs segmentation; (3) affine registration-based BMs labeling. Afterwards the client can display BMs contours with corresponding atlas labels, and allows further post-processing tasks including: (1) change window level; (2) display/hide specific contours; (3) remove false-positive contours; (4) export contours as DICOM RTStruct files; etc. RESULTS: We evaluate this platform on 10 clinical cases with BMs number varied from 12-81. The overall operation takes about 4-5 minutes per patient. The segmentation accuracy is evaluated between the manual contour and automatic segmentation with averaged center of mass shift as 1.55±0.36 mm, Hausdorff distance as 2.98±0.63 mm, the mean of surface-to-surface distance (SSD) as 1.06±0.31 mm and the standard deviation of SSD as 0.80±0.16 mm, and the initial averaged false-positive over union (FPoU) and false-negative rate (FNR) as 0.43±0.19 and 0.15±0.10, respectively. After case-specific post-processing, the averaged FPoU and FNR are 0.19±0.10 and 0.15±0.10, respectively. CONCLUSION A web-based BMs segmentation and labeling platform is developed and evaluated. Compared to manual segmentation/labeling, it can substantially improve the clinical efficiency. This platform can be a useful tool for assisting SRS treatment planning and treatment follow-up.

[1]  W. Hall,et al.  Brain metastases: Histology, multiplicity, surgery, and survival , 1996 .

[2]  J. Buckner,et al.  N107C/CEC.3: A Phase III Trial of Post-Operative Stereotactic Radiosurgery (SRS) Compared with Whole Brain Radiotherapy (WBRT) for Resected Metastatic Brain Disease , 2016 .

[3]  Hiroki Shirato,et al.  Stereotactic Radiosurgery Plus Whole-Brain Radiation Therapy vs Stereotactic Radiosurgery Alone for Treatment of Brain Metastases: A Randomized Controlled Trial , 2007 .

[4]  Chen-Ping Yu,et al.  3D blob based brain tumor detection and segmentation in MR images , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[5]  Jie Yang,et al.  Brain MRI segmentation with patch-based CNN approach , 2016, 2016 35th Chinese Control Conference (CCC).

[6]  Xinjian Chen,et al.  Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images , 2013, Medical Image Anal..

[7]  D. Moratal,et al.  Brain Metastases Detection Algorithms in Magnetic Resonance Imaging , 2016, IEEE Latin America Transactions.

[8]  Steve B. Jiang,et al.  Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications , 2016, Physics in medicine and biology.

[9]  Xenophon Papademetris,et al.  More accurate Talairach coordinates for neuroimaging using non-linear registration , 2008, NeuroImage.

[10]  Steve B. Jiang,et al.  A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery , 2017, PloS one.

[11]  B. Guthrie,et al.  Comparison of plan quality and delivery time between volumetric arc therapy (RapidArc) and Gamma Knife radiosurgery for multiple cranial metastases. , 2014, Neurosurgery.

[12]  Odelin Charron,et al.  Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network , 2018, Comput. Biol. Medicine.

[13]  Nelly Gordillo,et al.  State of the art survey on MRI brain tumor segmentation. , 2013, Magnetic resonance imaging.

[14]  H. Wadell,et al.  Volume, Shape, and Roundness of Quartz Particles , 1935, The Journal of Geology.

[15]  Ezequiel Geremia,et al.  Spatial Decision Forests for Glioma Segmentation in Multi-Channel MR Images , 2011 .

[16]  Mohammad Havaei,et al.  Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification , 2014, 2014 22nd International Conference on Pattern Recognition.

[17]  Min-Oh Kim,et al.  Computer-Aided Detection of Metastatic Brain Tumors Using Magnetic Resonance Black-Blood Imaging , 2013, Investigative radiology.

[18]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[19]  Angela R. Laird,et al.  Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: Validation of the Lancaster transform , 2010, NeuroImage.

[20]  M. Mehta,et al.  Current management of brain metastases, with a focus on systemic options. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[21]  et al.,et al.  ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI , 2017, Medical Image Anal..

[22]  O. Pastyr,et al.  Stereotactic percutaneous single dose irradiation of brain metastases with a linear accelerator. , 1987, International journal of radiation oncology, biology, physics.

[23]  Sébastien Jodogne,et al.  Orthanc - A lightweight, restful DICOM server for healthcare and medical research , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[24]  P. Brown,et al.  Whole-brain radiotherapy in the management of brain metastasis. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[25]  S Mutic,et al.  A framework for automated contour quality assurance in radiation therapy including adaptive techniques , 2015, Physics in medicine and biology.

[26]  Terry M. Peters,et al.  3D statistical neuroanatomical models from 305 MRI volumes , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

[27]  J L Lancaster,et al.  Automated Talairach Atlas labels for functional brain mapping , 2000, Human brain mapping.

[28]  Michael J. Martinez,et al.  Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template , 2007, Human brain mapping.

[29]  Rolf Bendl,et al.  Accuracy quantification of a deformable image registration tool applied in a clinical setting , 2014, Journal of applied clinical medical physics.

[30]  K. Hess,et al.  Neurocognition in patients with brain metastases treated with radiosurgery or radiosurgery plus whole-brain irradiation: a randomised controlled trial. , 2009, The Lancet. Oncology.

[31]  D. Hill,et al.  Non-rigid image registration: theory and practice. , 2004, The British journal of radiology.

[32]  James S. Duncan,et al.  Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and Labeling , 2012, STIA.

[33]  J. Pouliot,et al.  The need for application-based adaptation of deformable image registration. , 2012, Medical physics.

[34]  Adrian Holovaty,et al.  The Definitive Guide to Django: Web Development Done Right, Second Edition , 2009 .

[35]  Stefan Bauer,et al.  Integrated segmentation of brain tumor images for radiotherapy and neurosurgery , 2013, Int. J. Imaging Syst. Technol..

[36]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[37]  Bo Zhao,et al.  Optimization of Treatment Geometry to Reduce Normal Brain Dose in Radiosurgery of Multiple Brain Metastases with Single–Isocenter Volumetric Modulated Arc Therapy , 2016, Scientific Reports.

[38]  Paul M. Thompson,et al.  Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods , 2011, IEEE Transactions on Medical Imaging.

[39]  L Souhami,et al.  Single dose radiosurgical treatment of recurrent previously irradiated primary brain tumors and brain metastases: final report of RTOG protocol 90-05. , 2000, International journal of radiation oncology, biology, physics.