Abstract
Abstract. In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The concept of this algorithm is to place a medical device (catheter) in an empty cavity of an anatomical structure, for example, in a heart chamber, and then transform it. To create random transformations of the catheter, the algorithm uses a coordinate system that uniquely identifies each point regardless of the bend and the shape of the object. It is proposed to take a cylindrical coordinate system as a basis, modifying it by replacing the Z-axis with a spline along which the h-coordinate is measured. Having used the proposed algorithm, we generated new images with the catheter inserted into different heart cavities while varying its location and shape. Afterward, we compared the results of deep neural networks trained on the datasets comprised of real and synthetic data. The network trained on both real and synthetic datasets performed more accurate segmentation than the model trained only on real data. For instance, modified U-net trained on combined datasets performed segmentation with the Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real samples achieved the level of 86.5±3.6%. Using a synthetic dataset allowed decreasing the accuracy spread and improving the generalization of the model. It is worth noting that the proposed algorithm allows reducing subjectivity, minimizing the labeling routine, increasing the number of samples, and improving the heterogeneity.
Highlights
Many machine learning algorithms are fairly sensitive to the datasets used for training
When solving the problem of localization and segmentation of the distal end of the catheter inside the heart, we encountered the problem of insufficient data and weak representativeness. To solve this problem we propose a new algorithm for synthesizing echocardiography data with inserted medical devices
Once the real and synthetic datasets were obtained, the modified U-net was trained with different values of Real Data Ratio (RDR)
Summary
Many machine learning algorithms are fairly sensitive to the datasets used for training. Training and test samples come from the same statistical distribution. Whilst the paucity of flexible and rich enough datasets limits the ability of machine learning or statistical modeling techniques and leaves the algorithm generalization capability superficial. Synthetic datasets that are generated programmatically can help immensely in the field of machine learning. These datasets are not collected by any reallife survey or experiment. Their main purpose, is to be flexible and rich enough to help in conducting experiments with various classification, segmentation, and object detection algorithms
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