  We present W-Net, a novel Convolution Neural Network (CNN) framework that
employs raw ultrasound waveforms from each A-scan, typically referred to as
ultrasound Radio Frequency (RF) data, in addition to the gray ultrasound image
to semantically segment and label tissues. Unlike prior work, we seek to label
every pixel in the image, without the use of a background class. To the best of
our knowledge, this is also the first deep-learning or CNN approach for
segmentation that analyses ultrasound raw RF data along with the gray image.
International patent(s) pending [PCT/US20/37519]. We chose subcutaneous tissue
(SubQ) segmentation as our initial clinical goal since it has diverse
intermixed tissues, is challenging to segment, and is an underrepresented
research area. SubQ potential applications include plastic surgery, adipose
stem-cell harvesting, lymphatic monitoring, and possibly detection/treatment of
certain types of tumors. A custom dataset consisting of hand-labeled images by
an expert clinician and trainees are used for the experimentation, currently
labeled into the following categories: skin, fat, fat fascia/stroma, muscle and
muscle fascia. We compared our results with U-Net and Attention U-Net. Our
novel \emph{W-Net}'s RF-Waveform input and architecture increased mIoU accuracy
(averaged across all tissue classes) by 4.5\% and 4.9\% compared to regular
U-Net and Attention U-Net, respectively. We present analysis as to why the
Muscle fascia and Fat fascia/stroma are the most difficult tissues to label.
Muscle fascia in particular, the most difficult anatomic class to recognize for
both humans and AI algorithms, saw mIoU improvements of 13\% and 16\% from our
W-Net vs U-Net and Attention U-Net respectively.
