Mr. Ji Zhang Profile

Ji Zhang

at Yunnan Univ

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Proceedings Article | 16 February 2022 Paper

DFM-Net: a contextual inference network for T2-weighted image segmentation of the pelvis

Ji Zhang, Wenhua Qian, Dan Xu, Yuanyuan Pu

Proceedings Volume 12083, 120831O (2022) https://doi.org/10.1117/12.2623470

KEYWORDS: Image segmentation, Magnetic resonance imaging, Medical imaging, Tissues, Convolution, Bone, Performance modeling, Feature extraction, Convolutional neural networks, Surgery

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Segmenting the pelvic region accurately and quickly from T2-weighted MR images is beneficial for the treatment of pelvic-related diseases. Nevertheless, there are great challenges in segmenting the pelvic region due to its varying scale range, similar tissue strength, and blurred edges. Although the well-known encoder-decoder convolutional neural network has achieved great success in the field of medical image segmentation, its ordinary convolutional layer is insufficient to adequately extract and convey features. Moreover, this architecture does not make the full utilization of different scale contextual information and sometimes fails to capture long-range dependencies. To address these challenges, this paper proposes a DFM-Net which applies Dense Block as the basic module to extract features and enhance the transfer of features. Aiming at the problem of tissue strength similarity, the non-local operation Feature Similarity Module (FSM) was introduced to capture long-range dependencies of the pelvis structure. To enrich the extraction of global and local context information, a new multi-scale method is applied to optimize the model during the up-sampling process of the decoder. Finally, experiments and evaluations were performed on the T2-weighted MR image data set of the pelvis. The encouraging results show that our proposed method is superior to the other five different methods of segmentation.

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