IEEE Trans Pattern Anal Mach Intell - Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data.

Tópicos

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Resumo

We present a new statistical pattern recognition approach for the problem of the left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance re-sampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance, shape, and motion models that take into account all previous and current images and previous segmentation contours produced by the method. The new appearance and shape models decouple the affine and non-rigid segmentations of the left ventricle in order to reduce the running time complexity. The proposed motion model combines the systole and diastole motion patterns and an observation distribution built by a deep neural network. The functionality of our approach is evaluated using a data set of diseased cases containing 16 sequences and another data set of normal cases comprising four sequences, where both sets present long axis views of the left ventricle. Using a training set comprising diseased and healthy cases, we show that our approach produces more accurate results than current state-of-the-art endocardium tracking methods in two test sequences from healthy subjects. Using three test sequences containing different types of cardiopathies, we show that our method correlates well with inter-user statistics produced by four cardiologists.

Resumo Limpo

present new statist pattern recognit approach problem left ventricl endocardium track ultrasound data problem formul sequenti import resampl algorithm expect segment current time step estim base appear shape motion model take account previous current imag previous segment contour produc method new appear shape model decoupl affin nonrigid segment left ventricl order reduc run time complex propos motion model combin systol diastol motion pattern observ distribut built deep neural network function approach evalu use data set diseas case contain sequenc anoth data set normal case compris four sequenc set present long axi view left ventricl use train set compris diseas healthi case show approach produc accur result current stateoftheart endocardium track method two test sequenc healthi subject use three test sequenc contain differ type cardiopathi show method correl well interus statist produc four cardiologist

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