Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.

[1]  Da-Chuan Cheng,et al.  Computer-assisted system on mandibular canal detection , 2017, Biomedizinische Technik. Biomedical engineering.

[2]  T. Y. Kong,et al.  Topological Algorithms for Digital Image Processing , 1996 .

[3]  J. S. Sahambi,et al.  Modified active contour model and Random Walk approach for left ventricular cardiac MR image segmentation , 2011 .

[4]  Nassir Navab,et al.  Ultrasound confidence maps using random walks , 2012, Medical Image Anal..

[5]  Yutong Liu,et al.  A semi-automatic image segmentation method for extraction of brain volume from in vivo mouse head magnetic resonance imaging using Constraint Level Sets , 2009, Journal of Neuroscience Methods.

[6]  Stephen R Bowen,et al.  Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization , 2017, Journal of medical imaging.

[7]  T. Sørensen,et al.  A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .

[8]  Ausif Mahmood,et al.  Review of Deep Learning Algorithms and Architectures , 2019, IEEE Access.

[9]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[10]  Andreas A. Linninger,et al.  Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics , 2018, Comput. Biol. Medicine.

[11]  M. Ruschin,et al.  Improved dosimetric accuracy with semi‐automatic contour propagation of organs‐at‐risk in glioblastoma patients undergoing chemoradiation , 2019, Journal of applied clinical medical physics.

[12]  D. de Ruysscher,et al.  Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2013, Annals of oncology : official journal of the European Society for Medical Oncology.

[13]  Xinjian Chen,et al.  Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images , 2015, IEEE Transactions on Image Processing.

[14]  Chengdong Wu,et al.  A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks , 2020, Sensors.

[15]  Chuan-Pin Lu,et al.  Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm , 2020, Sensors.

[16]  M. Dehmer,et al.  An Introductory Review of Deep Learning for Prediction Models With Big Data , 2020, Frontiers in Artificial Intelligence.

[17]  Shing-Hong Liu,et al.  Accurate Measurement of Cross-Sectional Area of Femoral Artery on MRI Sequences of Transcontinental Ultramarathon Runners Using Optimal Parameters Selection , 2016, Journal of Medical Systems.

[18]  Edoardo Ardizzone,et al.  An enhanced random walk algorithm for delineation of head and neck cancers in PET studies , 2017, Medical & Biological Engineering & Computing.

[19]  Bin Wang,et al.  HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images , 2016, Journal of Medical Systems.

[20]  Te Vuong,et al.  Past, present, and future of radiotherapy for the benefit of patients , 2013, Nature Reviews Clinical Oncology.

[21]  Günter Rote,et al.  Computing the Minimum Hausdorff Distance Between Two Point Sets on a Line Under Translation , 1991, Inf. Process. Lett..

[22]  Yann LeCun,et al.  The mnist database of handwritten digits , 2005 .

[23]  Amit Choudhary,et al.  Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN) , 2020, Sensors.

[24]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.