Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information

Abstract

Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to apply machine learning to support fetal cardiac ultrasound screening; we have to detect cardiac substructures precisely which are small and change shapes dynamically with fetal heartbeats, such as the ventricular septum. This task is difficult for general segmentation methods such as DeepLab v3+, and U-net. Hence, here we proposed a novel segmentation method named Cropping-Segmentation-Calibration (CSC) that is specific to the ventricular septum in ultrasound videos in this study. CSC employs the time-series information of videos and specific section information to calibrate the output of U-net. The actual sections of the ventricular septum were annotated in 615 frames from 421 normal fetal cardiac ultrasound videos of 211 pregnant women who were screened. The dataset was assigned a ratio of 2:1, which corresponded to a ratio of the training to test data, and three-fold cross-validation was conducted. The segmentation results of DeepLab v3+, U-net, and CSC were evaluated using the values of the mean intersection over union (mIoU), which were 0.0224, 0.1519, and 0.5543, respectively. The results reveal the superior performance of CSC.

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

“…Currently, there are high expectations for the development of medical AI, and it is expected that AI technology will be actively introduced in actual clinical practice in the future. On the other hand, medical AI research for clinical applications is currently focused on medical image analysis (137)(138)(139)(140)(141)(142)(143)(144), and research on the introduction of AI to omics analysis such as whole genome analysis and epigenome analysis, as well as its clinical application, has not progressed sufficiently yet. In this regard, one of the problems associated with the widespread adoption of AI-based methodologies in omics analysis is that even though sequencing technology and other advanced analytics are increasingly being used in research and clinical practice, there is still a lot of confusion about the best protocols to adopt for analysis.…”

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

“…The interactive few-shot Siamese network uses a Siamese network and a recurrent neural network to perform 3D segmentation training from few- We introduce the specialized algorithms for US imaging analysis to address the performance deterioration owing to noisy artifacts. Cropping-segmentation-calibration (CSC) [51] and the multi-frame + cylinder method (MFCY) [52] use time-series information to reduce noisy artifacts and to perform accurate segmentation in US videos (Figure 3). Deep attention networks have also been proposed for improved segmentation performance in US imaging, such as the attention-guided dual-path network [53] and a U-Net-based network combining a channel attention module and VGG [54].…”

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities

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