Machine learning aids in detecting lung contour, reducing radiologist workload

Radiation therapy is an integral part of many cancer treatments. Ideally, doses are focused on the observable tumor while leaving surrounding organs unaffected, but determining the figuration of tumors and organs-at-risk is done manually--a time consuming and, at times, imprecise task for radiologists. A team of Chinese researchers developed a machine learning technique--closed polygonal line and backpropagation neural network model (CPL-BNNM)--for accurately detecting smooth lung contours in 3D-CT scans that is more efficient than manually determining such information and superior to currently used algorithms. "The important information for organ diseases can be quantitatively provided by the clinical images, while quantification is often manually implemented in some clinics," wrote Tao Peng, with the School of Computer Science & Technology at Soochow University. "In order to speed up the manual task and reduce workload, combining computer-aided diagnosis with automatic detection method is becoming a research hotspot."