Automatic segmentation of 3D prostate MR images with iterative localization refinement

Abstract Accurate segmentation of the prostate gland from Magnetic Resonance (MR) images is still a challenging problem due to large variability and heterogeneity in the prostate appearance. To overcome this problem, we present a coarse-to-fine prostate segmentation approach with iterative localization refinement. Specifically, we first propose a resolution-aware 3D U-shaped network to balance the difference between the in-plane resolution and the through-plane distance. Then a case-wise loss function is introduced to alleviate the data imbalance problem and individual differences of the prostate MR images. In the inference stage, we extract a shrunk prostate region and improve the segmentation results in an iterative manner. Evaluation experiments are carried out on the MICCAI 2012 Prostate Segmentation Challenge Dataset (PROMISE12) and the NCI-ISBI 2013 Prostate Segmentation Challenge Dataset. Comparison results demonstrate that our method achieves significant improvements over the state-of-the-art approaches, and outperforms more than 290 submissions on the website of PROMISE12.

[1]  Huiyan Jiang,et al.  AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes , 2019, IEEE Access.

[2]  Dinggang Shen,et al.  Segmentation of prostate boundaries from ultrasound images using statistical shape model , 2003, IEEE Transactions on Medical Imaging.

[3]  Xiaoou Tang,et al.  Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net , 2018, ECCV.

[4]  Giancarlo Mauri,et al.  USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets , 2019, Neurocomputing.

[5]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[6]  Joachim M. Buhmann,et al.  Prostate MRI Segmentation Using Learned Semantic Knowledge and Graph Cuts , 2014, IEEE Transactions on Biomedical Engineering.

[7]  Jürgen Weese,et al.  Deep Learning-Based Boundary Detection for Model-Based Segmentation with Application to MR Prostate Segmentation , 2018, MICCAI.

[8]  Xiangyu Zhang,et al.  Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Bo Du,et al.  Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation , 2019, IEEE Transactions on Medical Imaging.

[11]  Christopher Joseph Pal,et al.  Learning normalized inputs for iterative estimation in medical image segmentation , 2017, Medical Image Anal..

[12]  Hao Chen,et al.  VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images , 2017, NeuroImage.

[13]  Purang Abolmaesumi,et al.  Automatic high resolution segmentation of the prostate from multi-planar MRI , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[14]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[15]  Zhijian Song,et al.  Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[16]  Aaron Fenster,et al.  Dual optimization based prostate zonal segmentation in 3D MR images , 2014, Medical Image Anal..

[17]  Pei Wang,et al.  Focal Dice Loss and Image Dilation for Brain Tumor Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[18]  Yang Song,et al.  3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images , 2020, IEEE Transactions on Medical Imaging.

[19]  Qingmao Hu,et al.  Automatic Magnetic Resonance Image Prostate Segmentation Based on Adaptive Feature Learning Probability Boosting Tree Initialization and CNN-ASM Refinement , 2018, IEEE Access.

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[22]  Xiaoying Wang,et al.  Fully automatic segmentation on prostate MR images based on cascaded fully convolution network , 2018, Journal of magnetic resonance imaging : JMRI.

[23]  Ronald M. Summers,et al.  Active appearance model and deep learning for more accurate prostate segmentation on MRI , 2016, SPIE Medical Imaging.

[24]  Bo Du,et al.  A Deep Learning Health Data Analysis Approach: Automatic 3D Prostate MR Segmentation with Densely-Connected Volumetric ConvNets , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[25]  Yaozong Gao,et al.  Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching , 2016, IEEE Transactions on Medical Imaging.

[26]  Weidong Cai,et al.  HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images , 2019, MICCAI.

[27]  Florian Jung,et al.  Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge , 2014, Medical Image Anal..

[28]  Xiaoying Tang,et al.  Prostate Segmentation Using Z-Net , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[29]  Naeem Khalid Janjua,et al.  Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions , 2019, IEEE Access.

[30]  Septimiu E. Salcudean,et al.  Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.

[31]  Su Yang,et al.  Cascade Dense-Unet for Prostate Segmentation in MR Images , 2019, ICIC.

[32]  David Dagan Feng,et al.  Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging , 2018, Neurocomputing.

[33]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[34]  Anant Madabhushi,et al.  Multifeature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation , 2012, IEEE Transactions on Medical Imaging.

[35]  David Dagan Feng,et al.  Prostate segmentation in MR images using ensemble deep convolutional neural networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[36]  Yan Wang,et al.  A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans , 2016, MICCAI.

[37]  Hao Chen,et al.  Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images , 2017, AAAI.

[38]  Jianru Xue,et al.  A supervoxel‐based segmentation method for prostate MR images , 2017, Medical physics.

[39]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Zhenfeng Zhang,et al.  Superpixel-Based Segmentation for 3D Prostate MR Images , 2016, IEEE Transactions on Medical Imaging.

[41]  Giancarlo Mauri,et al.  Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging , 2017, Inf..

[42]  Chang-Su Kim,et al.  Comparison of objective functions in CNN-based prostate magnetic resonance image segmentation , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[43]  Su Yang,et al.  Scale Normalization Cascaded Dense-Unet for Prostate Segmentation in MR Images , 2019, ICIG.

[44]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  C. Davatzikos,et al.  Multi-Atlas Segmentation of the Prostate: A Zooming Process with Robust Registration and Atlas Selection , 2012 .

[46]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[47]  Christopher Joseph Pal,et al.  The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.

[48]  Desire Sidibé,et al.  A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images , 2012, Comput. Methods Programs Biomed..