Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer

Background@#In pancreatic cancer surgery, anatomical understanding of lymph node metastases is required. Distinguishing lymph nodes in computed tomography or magnetic resonance imaging is challenging for novice doctors and medical students because of their small size and similar color to surrounding tissues. This study aimed to enhance our understanding of the clinical anatomy of lymph node stations relevant to pancreatic cancer using newly sectioned images of a cadaver with true color and high resolution and their three-dimensional (3D) models. @*Methods@#An 88-year-old female cadaver who died of pancreatic cancer was serially sectioned.Among the sectioned images of the whole body (0.05 mm-sized pixel, 48 bits color), images of the abdomen were selected, and examined to identify lymph nodes and nearby structures.34 structures (9 in digestive system; 1 in urinary system; 2 in cardiovascular system; 22 in lymphatic system) were segmented on the sectioned images. Based on the sectioned and segmented images, volume and surface models were produced. @*Results@#Among the known 28 lymph node stations, 21 stations were identified through location, size, and color of normal and abnormal structures in the sectioned images and 3D models. Two near the splenic artery could not be separated from the cancer tissue, and the remaining five were not clearly identified. In the surface models, the shape and location of lymph node stations could be confirmed with nearby structures. @*Conclusion@#The lymph node stations relevant to pancreatic cancer can be anatomically understood by using the sectioned images and 3D models which contain true color and high resolution.

Identification of cranial nerve ganglia using sectioned images and three-dimensional models of a cadaver

Background@#Cranial nerve ganglia, which are prone to viral infections and tumors, are located deep in the head, so their detailed anatomy is difficult to understand using conventional cadaver dissection. For locating the small ganglia in medical images, their sectional anatomy should be learned by medical students and doctors.The purpose of this study is to elucidate cranial ganglia anatomy using sectioned images and three-dimensional (3D) models of a cadaver. @*Methods@#One thousand two hundred and forty-six sectioned images of a male cadaver were examined to identify the cranial nerve ganglia. Using the real color sectioned images, real color volume model having a voxel size of 0.4 × 0.4 × 0.4 mm was produced. @*Results@#The sectioned images and 3D models can be downloaded for free from a webpage, anatomy.dongguk.ac.kr/ganglia. On the images and model, all the cranial nerve ganglia and their whole course were identified. In case of the facial nerve, the geniculate, pterygopalatine, and submandibular ganglia were clearly identified.In case of the glossopharyngeal nerve, the superior, inferior, and otic ganglia were found. Thanks to the high resolution and real color of the sectioned images and volume models, detailed observation of the ganglia was possible. Since the volume models can be cut both in orthogonal planes and oblique planes, advanced sectional anatomy of the ganglia can be explained concretely. @*Conclusions@#The sectioned images and 3D models will be helpful resources for understanding cranial nerve ganglia anatomy, for performing related surgical procedures.

Dawn of the Visible Monkey: Segmentation of the Rhesus Monkey for 2D and 3D Applications

Background@#To properly utilize the sectioned images in a Visible Monkey dataset, it is essential to segment the images into distinct structures. This segmentation allows the sectioned images to be compiled into two-dimensional or three-dimensional software packages to facilitate anatomy and radiology education, and allows them to be used in experiments involving electromagnetic radiation. The purpose of the present study was to demonstrate the potential of the sectioned images using the segmented images. @*Methods@#Using sectioned images of a monkey's entire body, 167 structures were segmented using Adobe Photoshop. The segmented images and sectioned images were packaged into the browsing software. Surface models were made from the segmented images using Mimics. Volume models were made from the sectioned images and segmented images using MRIcroGL. @*Results@#In total, 839 segmented images of 167 structures in the entire body of a monkey were produced at 0.5-mm intervals (pixel size, 0.024 mm; resolution, 8,688 × 5,792; color depth, 24-bit color; BMP format). Using the browsing software, the sectioned images and segmented images were able to be observed continuously and magnified along with the names of the structures. The surface models of PDF file were able to be handled freely using Adobe Reader. In the surface models, the space information of all segmented structures was able to be identified using Sim4Life. On MRIcroGL, the volume model was able to be browsed and sectioned at any angle with real color. @*Conclusion@#Browsing software, surface models, and volume models are able to be produced based on the segmentation of the sectioned images. These will be helpful for students and researchers studying monkey anatomy and radiology, as well as for biophysicists examining the effects of electromagnetic radiation.

Application of Ecklonia cava Kjellman by-product as a feed additive: enhancing weight gain, immunity and protection from Salmonella infection in chickens

The Ecklonia cava Kjellman by-product (ECBP) as a feed additive was evaluated in improvement of productivity and immune enhancement against Salmonella Gallinarum (SG). Lohmann Brown chickens proved SG-free were randomly divided into 3 groups of 8 chickens each. Chickens were fed with the experimental diet treatment: T0, Non treatment-commercial feed; T1, commercial feed with 0.5% ECBP; T2, commercial feed with 0.1% Lactobacillus plantarum. In this study, we evaluated the effect of T1 and T2 groups on the body weight and protective efficacy against SG in chickens. The results demonstrated that treatment of T1 group as a feed additive affected significantly body weight gaining in chickens. In addition, T1 group showed a significant different colonization of SG when compared to T2 and T0 groups. We also studied that serum IgG and interferon-γ levels were significantly different compared with other treatment groups. Therefore, we suggest that ECBP can be used as a good candidate of feed additives in chicken industry.