FCRB U-Net: A novel fully connected residual block U-Net for fetal cerebellum ultrasound image segmentation

Prenatal Diagnostics Using Deep Learning: A Dual Approach to Plane Localization and Cerebellum Segmentation in Ultrasound Images

OBJECTIVE

The fetal ultrasound examination is the significant task of mid-term pregnancy inspection and the accurate localization as well as the segmentation of the cerebellum is crucial for clinical diagnosis. This research focuses on developing deep learning techniques for prenatal prediction of neurodevelopmental disorders using 5th-month ultrasound brain images.

METHODS

The study introduces two specialized convolutional neural network (CNN) architectures: the differential CNN for plane localization and the dual CNN for cerebellum segmentation which are critical for accurate diagnostics during prenatal care. The differential CNN incorporates six different convolutional operators to capture diverse features for precise localization of specific planes within images. The dual CNN architecture integrates both the original image and complementary information such as feature maps, to enhance segmentation accuracy for the cerebellum. The models are trained on annotated datasets of ultrasound images, validated, and tested on separate datasets.

RESULTS

The effectiveness of the proposed CNN architectures is determined by performing the specific tasks of plane localization and cerebellum segmentation, respectively. The proposed models achieved high performances of 98.6% and 0.956% from accuracy and dice coefficient (DSC) compared to existing approaches in medical image analysis.

CONCLUSION

The findings of this study have significant implications for the prenatal prediction of neurodevelopmental disorders, offering a promising advancement in prenatal care and early diagnostics. The custom CNN architectures tailored to the specific tasks of plane localization and cerebellum segmentation highlight the importance of task-specific model design in medical imaging. While the study acknowledges certain limitations and challenges, the power of deep learning is analyzed for marking the benefit of healthcare and neurodevelopmental disorder prediction during pregnancy.

CSCA U-Net: A channel and space compound attention CNN for medical image segmentation

Image segmentation is one of the vital steps in medical image analysis. A large number of methods based on convolutional neural networks have emerged, which can extract abstract features from multiple-modality medical images, learn valuable information that is difficult to recognize by humans, and obtain more reliable results than traditional image segmentation approaches. U-Net, due to its simple structure and excellent performance, is widely used in medical image segmentation. In this paper, to further improve the performance of U-Net, we propose a channel and space compound attention (CSCA) convolutional neural network, CSCA U-Net in abbreviation, which increases the network depth and employs a double squeeze-and-excitation (DSE) block in the bottleneck layer to enhance feature extraction and obtain more high-level semantic features. Moreover, the characteristics of the proposed method are three-fold: (1) channel and space compound attention (CSCA) block, (2) cross-layer feature fusion (CLFF), and (3) deep supervision (DS). Extensive experiments on several available medical image datasets, including Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS, CVC-T, 2018 Data Science Bowl (2018 DSB), ISIC 2018, and JSUAH-Cerebellum, show that CSCA U-Net achieves competitive results and significantly improves generalization performance. The codes and trained models are available at https://github.com/xiaolanshu/CSCA-U-Net.