Title: Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention Show Chinese title

Title: 基于特征金字塔注意力的全卷积网络自动前列腺分区分割

Abstract: Our main objective is to develop a novel deep learning-based algorithm for automatic segmentation of prostate zone and to evaluate the proposed algorithm on an additional independent testing data in comparison with inter-reader consistency between two experts. With IRB approval and HIPAA compliance, we designed a novel convolutional neural network (CNN) for automatic segmentation of the prostatic transition zone (TZ) and peripheral zone (PZ) on T2-weighted (T2w) MRI. The total study cohort included 359 patients from two sources; 313 from a deidentified publicly available dataset (SPIE-AAPM-NCI PROSTATEX challenge) and 46 from a large U.S. tertiary referral center with 3T MRI (external testing dataset (ETD)). The TZ and PZ contours were manually annotated by research fellows, supervised by genitourinary (GU) radiologists. The model was developed using 250 patients and tested internally using the remaining 63 patients from the PROSTATEX (internal testing dataset (ITD)) and tested again (n=46) externally using the ETD. The Dice Similarity Coefficient (DSC) was used to evaluate the segmentation performance. DSCs for PZ and TZ were 0.74 and 0.86 in the ITD respectively. In the ETD, DSCs for PZ and TZ were 0.74 and 0.792, respectively. The inter-reader consistency (Expert 2 vs. Expert 1) were 0.71 (PZ) and 0.75 (TZ). This novel DL algorithm enabled automatic segmentation of PZ and TZ with high accuracy on both ITD and ETD without a performance difference for PZ and less than 10% TZ difference. In the ETD, the proposed method can be comparable to experts in the segmentation of prostate zones. Show Chinese abstract

Abstract: 我们的主要目标是开发一种基于深度学习的新算法，用于前列腺区域的自动分割，并在与两名专家之间的读者一致性比较中，在额外的独立测试数据上评估所提出的算法。 在IRB批准和HIPAA合规的情况下，我们设计了一种新的卷积神经网络（CNN），用于在T2加权（T2w）MRI上自动分割前列腺移行区（TZ）和周围区（PZ）。 整个研究队列包括来自两个来源的359名患者；313名来自一个去标识化的公开数据集（SPIE-AAPM-NCI PROSTATEX挑战），46名来自一个大型美国三级转诊中心，使用3T MRI（外部测试数据集（ETD））。 TZ和PZ的轮廓由研究助理手动标注，由泌尿生殖（GU）放射科医生监督。 该模型使用250名患者进行开发，并使用剩余的63名来自PROSTATEX（内部测试数据集（ITD））的患者进行内部测试，并使用ETD（n=46）再次进行外部测试。 使用Dice相似性系数（DSC）来评估分割性能。 ITD中PZ和TZ的DSC分别为0.74和0.86。 在ETD中，PZ和TZ的DSC分别为0.74和0.792。 读者间的一致性（专家2 vs. 专家1）分别为0.71（PZ）和0.75（TZ）。 这种新的DL算法能够在ITD和ETD上以高精度自动分割PZ和TZ，PZ没有性能差异，TZ的差异小于10%。 在ETD中，所提出的方法在前列腺区域的分割方面可以与专家相媲美。