摘要：AbstractUtilizing machine learning techniques for classification of tumorous tissue has been shown to be extremely effective for external imaging and processing. However, when training an algorithm to work intraoperatively for identifying the extent of cancerous tissue within the body using intraoperative sensors, an adequate data set is not readily available or in many cases not feasible to obtain. For this reason, accurate and realistic simulations of cancer growth can be a powerful tool in supplementing datasets where it is not realistic to directly obtain sensor measurements in the body. This work introduces a simulation environment to generate cell-based cancerous tumors grown within a controlled oxygen environment. The growth and oxygen simulations are outlined along with how they are implemented within an open source simulation environment. Simulation results are provided showing the influence of the oxygen field on tumor growth shape and rate. The results follow intuitive growth characteristics and exhibit invasive behavior from lack of oxygen. The simulation environment lends itself to be easily and flexibly used for further detailed investigations of tumor growth.
