SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes
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Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification. Delineation of the anatomical boundary of organs and pathological lesions is quite challenging due to the stochastic nature of speckle intensity in the images, which also introduces visual fatigue for the observer. This paper introduces a fully convolutional neural network based method to segment organ and pathologies in ultrasound volume by learning the spatial-relationship between closely related classes in the presence of stochastically varying speckle intensity. We propose a convolutional encoder-decoder like framework with (i) feature concatenation across matched layers in encoder and decoder and (ii) index passing based unpooling at the decoder for semantic segmentation of ultrasound volumes. We have experimentally evaluated the performance on publicly available datasets consisting of 10 intravascular ultrasound pullback acquired at 20 MHz and 16 freehand thyroid ultrasound volumes acquired 11 - 16 MHz. We have obtained a dice score of 0.93 ± 0.08 and 0.92 ± 0.06 respectively, following a 10-fold cross-validation experiment while processing frame of 256 × 384 pixel in 0.035s and a volume of 256 × 384 × 384 voxel in 13.44s.
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