Automatic segmentation of rectal tumor on diffusion‐weighted images by deep learning with U‐Net

Abstract

Purpose Manual delineation of a rectal tumor on a volumetric image is time‐consuming and subjective. Deep learning has been used to segment rectal tumors automatically on T2‐weighted images, but automatic segmentation on diffusion‐weighted imaging is challenged by noise, artifact, and low resolution. In this study, a volumetric U‐shaped neural network (U‐Net) is proposed to automatically segment rectal tumors on diffusion‐weighted images. Methods Three hundred patients of locally advanced rectal cancer were enrolled in this study and divided into a training group, a validation group, and a test group. The region of rectal tumor was delineated on the diffusion‐weighted images by experienced radiologists as the ground truth. A U‐Net was designed with a volumetric input of the diffusion‐weighted images and an output of segmentation with the same size. A semi‐automatic segmentation method was used for comparison by manually choosing a threshold of gray level and automatically selecting the largest connected region. Dice similarity coefficient (DSC) was calculated to evaluate the methods. Results On the test group, deep learning method (DSC = 0.675 ± 0.144, median DSC is 0.702, maximum DSC is 0.893, and minimum DSC is 0.297) showed higher segmentation accuracy than the semi‐automatic method (DSC = 0.614 ± 0.225, median DSC is 0.685, maximum DSC is 0.869, and minimum DSC is 0.047). Paired t‐test shows significant difference (T = 2.160, p = 0.035) in DSC between the deep learning method and the semi‐automatic method in the test group. Conclusion Volumetric U‐Net can automatically segment rectal tumor region on DWI images of locally advanced rectal cancer.

Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer

“…Interestingly, recent studies presenting DL algorithms for automated MRI tumor segmentations of other pelvic malignancies report performance metrics with DSCs in the range of 0.52-0.84 [35][36][37][38][39], i.e., prostate cancer (DSC of 0.52 using k-fold cross-validation [35] [n = 204]), endometrial cancer (DSC of 0.77/0.84 using a test set [36] [n = 139] and DSC of 0.81 using k-fold cross-validation [37] [n = 200]), and rectal cancer (DSC of 0.68/0.70 using a test set [38] [n = 140] and DSC of 0.70 using a test set [39] [n = 300]). Hence, our DSCs for the DL algorithm in CC (DL-R1: median DSC = 0.60, DL-R2: DSC = 0.58) are quite comparable to that of other pelvic malignancies.…”

Combining Diffusion-Weighted Imaging and T2-Weighted Imaging to Delineate Tumorous Tissue in Peritoneal Carcinomatosis: A Comparative Study with 18F-Fluoro-Deoxyglucose Positron Emission Tomography with Computed Tomography (FDG PET/CT)

“…Apart from that, whether the combination of T2WI provides additional value to the increase of delineation accuracy still remains to be discussed. Some researchers proposed the combination of T2WI and DWI to balance the limited spatial resolution of DWI [ 12 , 33 , 34 ]. The combination of T2WI+DWI+ADC exhibited the best performance [ 35 ].Using an ADC map alone, the estimated volume of the HCTV ADC in the present study was less satisfactory.…”

Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

“…Hai-Tao Zhu et al. proposed a volumetric U-Net model that can automatically segment the rectal tumor region on the diffusion-weighted imaging images of LARC ( 29 ).…”

“…The results show that deep learning auto-segmentations were better than that of Atlas and can be used clinically. Hai-Tao Zhu et al proposed a volumetric U-Net model that can automatically segment the rectal tumor region on the diffusion-weighted imaging images of LARC (29).…”