ABSTRACT
The concept of precision medicine has brought revolutionary changes to human health and medical treatment. In a broad sense, precision medicine is the application of advanced technologies such as gene sequencing, digital medicine, artificial intelligence, navigation technology, and biomedical engineering to clinical, basic, and public health medical research and practice. The purpose is to promote the continuous development of medical standards. The concept of precision surgery was first proposed by academician Dong Jiahong, who has played an exemplary role in the field of liver surgery for the oncology and other professional fields of surgery. The development and application of computer-assisted surgery systems have promoted the development of precision liver surgery. The digital liver three-dimensional reconstruction provides a new basis for liver vascular anatomy and liver segmentation, assists in the realization of individualized surgical planning, and the measurement of residual liver volume guarantees the safety of operation. The great progress of adult precision liver surgery has provided important help and guidance for the development of pediatric liver surgery. Compared with adults, pediatric liver tumors have their particularities. Pediatric liver tumors are often huge, complex in location, fast growing, and highly malignant. Most children do not have liver cirrhosis and are sensitive to chemotherapy. The liver volume changes greatly with age and weight. These differences determine that the treatment of liver tumors in children needs to adopt different models, and the diagnosis and treatment standards need to be improved. The authors elaborate on the topic of "from adult to child-the development and prospect of precision liver surgery" to promote precision surgery and comprehensive treatment of liver tumors.
ABSTRACT
Digital intelligent diagnostic and treatment technology refers to a novel technology which is based by associating digitalized and intelligent high-tech with modern science to form a multi-knowledge and multi-disciplinary domain; it includes three-dimensional visualization,3 D printing,molecular fluorescence imaging,mixed reality,photoacoustic imaging,artificial intelligence-radiomis and real-time multimodal image surgical navigation.It plays a significant role in precision diagnosis,preoperative planning and intraoperative navigation of diseases.The authors' team,combining digital intelligent technology with the actual vascular distribution of patients,has been committed to the realization of individualized liver segmentation,volume calculation,simulation surgery,preoperative planning,mixed reality,tumor boundary definition and real-time image fusion for navigation in liver surgery.The research results were applied clinically,and achieved anatomical,functional,and radical hepatectomy for liver tumors.
ABSTRACT
Objective To determine the value of liver analysis application in liver segmentation and planning of surgery.Methods Thirty patients suspected having hepatic disease were recruited in this study.Contrast-enhanced CT examinations were performed with Philips Brilliance 64-slice CT,and multi-phase images were obtained.The patients were divided into group B(with focal hepatic lesion,15 patients),and group A(without hepatic lesion,15 patients).We use the portal-venous(60-70 s)images to analysis.Liver volume and vessel recognition were edited manually if necessary,then liver segmentation proceeded automatically.All data were analyzed by the t test,chi-square test,Mann-Whitney U analysis,with SPSS 15.0 software.Results There was no significant difference of post-processing procedure between the two groups(P =0.361).The liver volume was(1374.61 ±444.05)cm3 in the group B and(1225.70±272.07)cm3 in the group A(P=0.108).The accuracy of vessel recognition was no significant difference between the two groups(P=0.87).21 vessels were recognized incorrectly include 18 hepatic veins.Conclusion The liver analysis application provides a 3D reconstruction allow vivid observation of liver segmentation and accurate estimation of the liver volume.It has broad prospect in diagnosing and surgical planning of the liver disease.
ABSTRACT
El volumen del hígado es un parámetro determinante en cirugía para la extracción de tumores, trasplantes, y en tratamientos de regeneración. Generalmente, la estimación de este volumen se calcula a partir de segmentaciones manuales realizadas por especialistas, siendo éste un proceso tedioso y con poca reproducibilidad de sus resultados. En este trabajo se presenta un método semiautomático para la segmentación del volumen del hígado en imágenes de TAC. El método consiste en superponer manualmente una superficie de triángulos en las imágenes, y deformarla por medio de una ecuación de movimiento asociada a cada uno de sus vértices, para delimitar las fronteras del hígado. La dinámica de la superficie depende de información de intensidades y gradientes, y de relaciones de vecindad entre los vértices, hasta cumplir un número de iteraciones. Comparaciones entre las segmentaciones del método con las segmentaciones de referencia en 20 estudios de TAC, muestran la adaptabilidad de la superficie a la forma y fronteras difusas del hígado, dos de los principales problemas de la segmentación.
Liver volume is a significant parameter in surgery for tumor extraction, transplants, and regeneration treatments. Generally, the volume estimation is obtained from manual segmentations performed by specialists, resulting in a tedious process with low reproducibility. In this work a semi-automatic method for the liver volume segmentation in CT images is presented. The method consist in manually superimpose a triangular surface on the images, and use a movement equation associated to each vertex to deform the surface and delimit the liver boundaries. Surface dynamics depend on intensity and gradient information, and neighboring relationships between vertices, until a fixed number of iterations is reached. Comparison between the obtained results and reference segmentations in 20 CT scans, show the surface adaptability to the shape and the diffuse boundaries of the liver, two of the principal segmentation problems.
ABSTRACT
Living donor liver transplantation is increasingly performed as an alternative to cadaveric transplantation. Preoperative screening of the donor candidates is very important. The quality, size, and vascular and biliary anatomy of the liver are best assessed with magnetic resonance (MR) imaging or computed tomography (CT). In particular, the volume of the potential graft must be measured to ensure sufficient liver function after surgery. Preoperative liver segmentation has proved useful for measuring the graft volume before living donor liver transplantations in previous studies. In these studies, the liver segments were manually delineated on each image section. The delineated areas were multiplied by the section thickness to obtain volumes and summed to obtain the total volume of the liver segments. This process is tedious and time consuming. To compensate for this problem, automatic segmentation techniques have been proposed with multiplanar CT images. These methods involve the use of sequences of thresholding, morphologic operations (ie, mathematic operations, such as image dilation, erosion, opening, and closing, that are based on shape), and 3D region growing methods. These techniques are complex but require a few computation times. We made a phantom for volume measurement with pig and evaluated actual volume of spleen and liver of phantom. The results represent that our semiautomatic volume measurement algorithm shows a good accuracy and repeatability with actual volume of phantom and possibility for clinical use to assist physician as a measuring tool.