Importance

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Abstract

Provided is a technique for automatically extracting a cross section with a high degree of precision and at high speed, with avoiding problems of operator dependence and imaging target dependence, from 3D volume data or temporally sequential 2D or 3D images or 3D volume data, acquired by a medical imaging device, when determining the cross section used for diagnosis and measurement. An image processor of an imaging device is provided with a cross section extractor for extracting a specified cross section from imaged data. The cross section extractor determines the specified cross section by using a learning model trained in advance to output discrimination scores for a plurality of cross sectional image data, the discrimination score representing spatial or temporal proximity to the specified cross section. The learning model is a downsized model obtained by integrating a highly trained model having a large number of layers, with an untrained model having less number of layers, followed by retraining.

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First Claim