I. Introduction
Colorectal cancer strikes more than 1.4 million people and accounts for 694 000 deaths globally in 2012 [1]. It is more common in developed countries, for example, in the USA, colorectal cancer is the second leading cause of cancer-related mortalities [2]. In the current clinical routine of radiotherapy, due to the advantages of magnetic resonance (MR) imaging for soft tissue enhancement [3], colorectal cancer regions are manually recognized and delineated from volumetric MR images acquired for treatment, including surgery and radiation therapy. However, this procedure is laborious, time consuming, and observer dependent, thus suffers from the tedious effort and limited reproducibility. Therefore, automatic colorectal tumor detection and segmentation methods are highly demanded to improve the clinical routine.