CNN Architecture for Surgical Image Segmentation Systems with Recursive Network Structure to Mitigate Overfitting Laparoscopic surgery, a minimally invasive cameraaided surgery, is performed commonly. However, it requires a camera assistant who holds and maneuvers a laparoscope. If the laparoscope can be controlled automatically using a robot, a surgeon can perform the operation without a camera assistant, which would be beneficial in the areas suffering from lack of surgeons. In this paper, a prototype image segmentation architecture, based on a convolutional neural network, is proposed to realize an automatic laparoscope control for cholecystectomy. Since the learning dataset is annotated manually by a few surgeons, its scale is currently quite limited. Therefore, we devised a recursive network structure, with some subnetworks which are used multiple times, to mitigate overfitting. Furthermore, instead of the common transposed convolution, the flipbased subpixel reconstruction is introduced into upsampling layers. Evaluation results reveal that these improvements bring better classification accuracy without increasing the number of parameters. The system shows a throughput sufficient for realtime laparoscope robot control with a single NVIDIA GTX 1080 GPU.

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.