Adaptation and Evaluation of Deep Learning Techniques for Skin Segmentation on Novel Abdominal Dataset Skin segmentation plays an important role in a wide variety of biomedical image processing applications, such as skin cancer identification, skin lesion detection, and wound isolation. However, contemporary research has been mainly based on facial and hand skin datasets, with no other body regions considered for skin pixels sampling. Segmenting skin specifically in the abdominal region can aid in robotic abdominal surgeries and treatment procedures, such as robotassisted laparoscopic surgeries and abdominal ultrasounds. A robust and highly accurate abdominal skin detection technique thus becomes imperative. To this end, we compiled a novel dataset of 1,400 segmented abdominal pictures and adapted and compared four abdominal skin segmentation techniques: one based on thresholding and three deep learning techniques, namely a fully connected neural network for pixellevel classification, and two convolutionbased networks, UNet and MaskRCNN. We show that the UNet model outperforms the other segmentation techniques, resulting in a pixeltopixel mean crossvalidation accuracy of 95.51% on our Abdominal dataset. The incorporation of the Abdominal dataset in the training helped improve the abdominal skin segmentation accuracy by 10.19%. The UNet model proved to be computationally the fastest, enabling real time skin segmentation with a processing rate of 37 frames per second.

Tumor Segmentation Based on Deeply Supervised MultiScale UNet Although deep learning has achieved great success in the field of medical image processing, the existing deep learning based medical image segmentation solutions still cannot obtain satisfactory performances for abdominal small organs and lesions due to their small object size and shapevariability. In this work, a Deeply Supervised MultiScale UNet (DSMS UNet) is proposed for more accurate segmentation performances on abdominal small organs images. DSMS UNet integrate the existing UNet model with a restoration decoder module and some multiscale convolution modules. Our experiment results demonstrate that the proposed DSMS UNet approach has much better segmentation performances than the stateoftheart baselines.