  We present an accurate, fast and efficient method for segmentation and muscle
mask propagation in 3D freehand ultrasound data, towards accurate volume
quantification. A deep Siamese 3D Encoder-Decoder network that captures the
evolution of the muscle appearance and shape for contiguous slices is deployed.
We uses it to propagate a reference mask annotated by a clinical expert. To
handle longer changes of the muscle shape over the entire volume and to provide
an accurate propagation, we devise a Bidirectional Long Short Term Memory
module. Also, to train our model with a minimal amount of training samples, we
propose a strategy combining learning from few annotated 2D ultrasound slices
with sequential pseudo-labeling of the unannotated slices. We introduce a
decremental update of the objective function to guide the model convergence in
the absence of large amounts of annotated data. After training with a small
number of volumes, the decremental update transitions from a weakly-supervised
training to a few-shot setting. Finally, to handle the class-imbalance between
foreground and background muscle pixels, we propose a parametric Tversky loss
function that learns to adaptively penalize false positives and false
negatives. We validate our approach for the segmentation, label propagation,
and volume computation of the three low-limb muscles on a dataset of 61600
images from 44 subjects. We achieve a Dice score coefficient of over $95~\%$
and a volumetric error \textcolor{black}{of} $1.6035 \pm 0.587~\%$.
