A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy
By Emran Mohammad Abu Anas, Purang Mousavi, Purang Abolmaesumi. 2018
89 citations recorded by the source corpus.
HighlightsReal‐time prostate segmentation to enable a targeted prostate biopsy system.Convolutional neural networks for extracting spatial features and recurrent neural networks for temporal features.Improved architecture and training of the network to provide better segmentation. Graphical abstract Figure. No caption available. ABSTRACT Targeted prostate biopsy, incorporating multi‐parametric magnetic resonance imaging (mp‐MRI) and its registration with ultrasound, is currently the state‐of‐the‐art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi‐automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real‐time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp‐MRI and ultrasound data. In addition to using convolutional networks for extracting spatial features, the proposed approach employs recurrent networks to exploit the temporal information among a series of ultrasound images. One of the key contributions in the architecture is to use residual convolution in the recurrent networks to improve optimization. We also exploit recurrent connections within and across different layers of the deep networks to maximize the utilization of the temporal information. Furthermore, we perform dense and sparse sampling of the input ultrasound sequence to make the network robust to ultrasound artifacts. Our architecture is trained on 2,238 labeled transrectal ultrasound images, with an additional 637 and 1,017 unseen images used for validation and testing, respectively. We obtain a mean Dice similarity coefficient of 93%, a mean surface distance error of 1.10 mm and a mean Hausdorff distance error of 3.0 mm. A comparison of the reported results with those of a state‐of‐the‐art technique indicates statistically significant improvement achieved by the proposed approach.
Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine
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Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “registration frame-work for registration of preoperative 3D data and intraoperative 2D X-ray images in image-guided therapy [99]; real-time prostate segmentation during targeted prostate biopsy, utilizing temporal information in the series of ultrasound images [100].”