  Transrectal ultrasound (US) is the most commonly used imaging modality to
guide prostate biopsy and its 3D volume provides even richer context
information. Current methods for 3D volume reconstruction from freehand US
scans require external tracking devices to provide spatial position for every
frame. In this paper, we propose a deep contextual learning network (DCL-Net),
which can efficiently exploit the image feature relationship between US frames
and reconstruct 3D US volumes without any tracking device. The proposed DCL-Net
utilizes 3D convolutions over a US video segment for feature extraction. An
embedded self-attention module makes the network focus on the speckle-rich
areas for better spatial movement prediction. We also propose a novel case-wise
correlation loss to stabilize the training process for improved accuracy.
Highly promising results have been obtained by using the developed method. The
experiments with ablation studies demonstrate superior performance of the
proposed method by comparing against other state-of-the-art methods. Source
code of this work is publicly available at
https://github.com/DIAL-RPI/FreehandUSRecon.
