A weakly supervised registration-based framework for prostate segmentation via the combination of statistical shape model and CNN

• URL: http://arxiv.org/abs/2007.11726v2
• Date: Thu, 30 Jul 2020 06:53:19 GMT
• Title: A weakly supervised registration-based framework for prostate segmentation via the combination of statistical shape model and CNN
• Authors: Chunxia Qin, Xiaojun Chen, Jocelyne Troccaz
• Abstract summary: We propose a weakly supervised registration-based framework for the precise prostate segmentation. An inception-based neural network (SSM-Net) was exploited to predict the model transform, shape control parameters and a fine-tuning vector. A residual U-net (ResU-Net) was employed to predict a probability label map from the input images.
• Score: 4.404555861424138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise determination of target is an essential procedure in prostate interventions, such as the prostate biopsy, lesion detection and targeted therapy. However, the prostate delineation may be tough in some cases due to tissue ambiguity or lack of partial anatomical boundary. To address this problem, we proposed a weakly supervised registration-based framework for the precise prostate segmentation, by combining convolutional neural network (CNN) with statistical shape model (SSM). To obtain the prostate region, an inception-based neural network (SSM-Net) was firstly exploited to predict the model transform, shape control parameters and a fine-tuning vector, for the generation of prostate boundary. According to the inferred boundary, a normalized distance map was calculated. Then, a residual U-net (ResU-Net) was employed to predict a probability label map from the input images. Finally, the average of the distance map and the probability map was regarded as the prostate segmentation. After that, two public dataset PROMISE12 and NCI- ISBI 2013 were utilized for the model computation and for the network training and testing. The validation results demonstrate that the segmentation framework using a SSM with 9500 nodes achieved the best performance, with a dice of 0.904 and an average surface distance of 1.88 mm. In addition, we verified the impact of model elasticity augmentation and fine-tuning item on the network segmentation capability. As a result, both factors have improved the delineation accuracy, with dice increased by 10% and 7% respectively. In conclusion, via the combination of two weakly supervised neural networks, our segmentation method might be an effective and robust approach for prostate segmentation.

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