A Novel Deep Learning Based Auto Segmentation For Rectum Tumor On MRI Image

Abstract

Manually contouring gross tumor volume (GTV) is a crucial and time-consuming process in rectum cancer radiotherapy. This study aims to develop a simple deep learning based rectum tumor auto segmentation algorithm on MRI T2 images. MRI scans (3T, T2-weighted) of 93 patients with locally advanced (cT3-4 and/or cN1-2) rectal cancer treated with neoadjuvant chemoradiotherapy followed by surgery were enrolled in this study. A very deep convolutional networks (VGG16) based full convolution network (FCN) was established as training model. The model was trained in two phases to increase efficiency, including tumor recognition and tumor segmentation. An opening (erosion and dilation) process was implemented to make contours smoother. Data were randomly separated into training (90%) and validation (10%) dataset for 10 folder cross-validation. Additionally, 20 patients were double contoured for performance evaluation. Four indices were calculated to evaluate the similarity of automated and manual segmentation, including Hausdorff distance (HD), average surface distance (ASD), Dice index (DSC) and Jaccard index (JSC). The DSC, JSC, HD, ASD (mean±SD) were 0.74±0.14, 0.60±0.16, 20.44±13.35mm and 3.25±1.69mm for validation dataset; And these indices were 0.71±0.13, 0.57±0.15, 14.91±7.62mm and 2.67±1.46mm between two human radiation oncologists. T-test suggested there is no statistically significant difference between automated segmentation and manual segmentation considering DSC (p=0.42), JSC (p=0.35), HD (p=0.079) and ASD (p=0.16). However, significant difference was found for HD (p=0.0027) without opening process. This study showed that a simple deep learning neural network can perform a human comparable segmentation for rectum cancer based on MRI T2 images.