TMD-Unet: Triple-Unet with Multi-Scale Input Features and Dense Skip Connection for Medical Image Segmentation.

Deep learning is one of the most effective approaches to medical image processing applications. Network models are being studied more and more for medical image segmentation challenges. The encoder-decoder structure is achieving great success, in particular the Unet architecture, which is used as a baseline architecture for the medical image segmentation networks.

Corrigendum to "Speed Improvement in Image Stitching for Panoramic Dynamic Images during Minimally Invasive Surgery".

[This corrects the article DOI: 10.1155/2018/3654210.].

Speed Improvement in Image Stitching for Panoramic Dynamic Images during Minimally Invasive Surgery.

Minimally invasive surgery (MIS) minimizes the surgical incisions that need to be made and hence reduces the physical trauma involved during the surgical process. The ultimate goal is to reduce postoperative pain and blood loss as well as to limit the scarring area and hence accelerate recovery. It is therefore of great interest to both the surgeon and the patient.