Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

By Qikui Zhu, Bo Du, Pingkun Yan. 2019

191 citations recorded by the source corpus.

Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. Recently, as deep learning, especially convolutional neural networks (CNNs), emerging as the best performed methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced than ever. Since large-scale dataset is one of the critical components for the success of deep learning, lack of sufficient training data makes it difficult to fully train complex CNNs. To tackle the above challenges, in this paper, we propose a boundary-weighted domain adaptive neural network (BOWDA-Net). To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss is proposed. Furthermore, an advanced boundary-weighted transfer leaning approach is introduced to address the problem of small medical imaging datasets. We evaluate our proposed model on three different MR prostate datasets. The experimental results demonstrate that the proposed model is more sensitive to object boundaries and outperformed other state-of-the-art methods.

Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine

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Citations in the corpus

Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation (2021, background): “One major class of loss functions that were not included were boundary-based loss functions (Kervadec et al., 2019; Zhu et al., 2019a), which are another class of loss functions that instead use distance-based metrics to optimise contours rather than distributions or regions used by cross entropy…”
Domain Adaptation for Medical Image Analysis: A Survey (2021, unknown): “Several techniques including CycleGAN [37], [114] and disentangle learning [36], [141] have been introduced into this emerging area, while further exploration of multi-modality DA is required for medical image analysis.”
MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data (2020, background): “Zhu et al. [38] developed a boundary-aware adversarial learning strategy to improve the boundary delineation on source domain by introducing external datasets.”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, unknown): “The authors in Zhu, Du, and Yan (2019) design a novel technique called Boundary-Weighted Domain Adaptive Neural Network (BOWDA-Net) which eases the boundary detection in segmentation by implementing boundary-weighted segmentation loss.”