Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks.
Da-Chuan ChengJen-Hong ChiShih-Neng YangShing-Hong LiuPublished in: Sensors (Basel, Switzerland) (2020)
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.
Keyphrases
- deep learning
- spinal cord
- computed tomography
- convolutional neural network
- dual energy
- image quality
- weight loss
- contrast enhanced
- positron emission tomography
- early stage
- machine learning
- diffusion weighted imaging
- spinal cord injury
- heart failure
- healthcare
- radiation therapy
- locally advanced
- neuropathic pain
- magnetic resonance imaging
- radiation induced
- single cell
- advanced cancer
- palliative care
- neural network
- atrial fibrillation
- cell therapy
- high throughput
- magnetic resonance
- squamous cell carcinoma
- mesenchymal stem cells
- risk assessment
- stem cells
- combination therapy
- pet ct
- children with cerebral palsy