Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information.
Ai DozenMasaaki KomatsuAkira SakaiReina KomatsuKanto ShozuHidenori MachinoSuguru YasutomiTatsuya ArakakiKen AsadaSyuzo KanekoMatsuoka RyuDaisuke AokiAkihiko SekizawaRyuji HamamotoPublished in: Biomolecules (2020)
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.
Keyphrases
- deep learning
- machine learning
- left ventricular
- convolutional neural network
- artificial intelligence
- magnetic resonance imaging
- heart failure
- pregnant women
- big data
- catheter ablation
- gene expression
- computed tomography
- dna methylation
- loop mediated isothermal amplification
- genome wide
- atrial fibrillation
- healthcare
- electronic health record
- social media
- sensitive detection
- real time pcr
- data analysis
- virtual reality