Lung cancer is the leading cause of cancer death in the United States. Successful treatment depends on a radiation oncologist’s ability to accurately measure the tumor’s shape and responsiveness to interventions. Furthermore, manual delineation of tumors is time consuming and prone to inconsistency or bias. The goal of this project was to produce, through a series of competitions on Topcoder in 2016 and 2017 (see the problem statement for Part 1; problem statement for Part 2), an automatic tumor delineation algorithm that parallels the accuracy of an average expert while exceeding an expert in terms of speed and consistency.
In Part 1, contestants were tasked with locating and contouring the tumor on images. Thirty-one competitors submitted 244 solutions and competed for $35,000 in prizes. The top solution was able to locate about 70% of the tumors. In Part 2, contestants were asked to improve the contouring performance of their solutions, given a point within the tumor as additional data. Eleven competitors submitted 164 solutions and competed for $15,000. Part 2 yielded a 40% improvement in performance from Part 1. For more information, see here.
