Abstract
Automatic segmentation of uterus and fetus from 3D fetal ultrasound images remains a challenging problem due to multiple issues of fetal ultrasound, e.g., the relatively low image quality, intensity variations. In this work, we present a novel framework for the joint segmentation of uterus and fetus. It consists of two main components: a task-specific fully convolutional neural network (FCN) and a bidirectional convolutional LSTM (BiCLSTM). Our framework is inspired by a simple observation: the segmentation task can be decomposed into multiple easier-to-solve subproblems. More specifically, the encoder of the FCN extracts object-relevant features from the ultrasound slices. The BiCLSTM layer is responsible for modeling the inter-slice correlations. The final two branches of the FCN decoder produce the uterus and fetus predictions. In this way, the burden of the whole problem is evenly distributed among different parts of our network, thereby maximally exploiting the capacity of our network. Furthermore, we propose a spatially constrained loss to restrict the spatial positions of the segmented uterus and fetus to boost the performance. Quantitative results demonstrate the effectiveness of the proposed method.
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This work was supported by Shenzhen Science and Technology Program (Grant No. KQTD2016112809330877).
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Kong, B. et al. (2022). Ultrasonography Uterus and Fetus Segmentation with Constrained Spatial-Temporal Memory FCN. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_19
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