Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

2 Biocybernetics and Biomedical Engineering

2 2020

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

A practical pretreatment planning method of multiple puncturing for thermal ablation surgery

100%

Li J. , Xu Y. , Shen N. , Feng L. , Ran Z. , Deng Z.

Biocybernetics and Biomedical Engineering

2020

tom Vol. 40, no. 4

1469--1485

Thermal ablation surgery serves as one of the main approaches to treat liver tumors. The pretreatment planning, which highly demands the experience and ability of the physician, plays a vital role in thermal ablation surgery. The planning of multiple puncturing is necessary for avoiding the possible interference, destroying the tumor thoroughly and minimizing the damage to healthy tissue. A GPU-independent pretreatment planning method is proposed based on multi-objective optimization, which takes the most comprehensive constraints into consideration. An adaptive decision method of closing kernel size based on Jenks Natural Breaks is utilized to describe the final feasible region more accurately. It should be noted that the reasonable procedure of solving the feasible region and the use of KD tree based high dimensional search approach are used to enhance the computational efficiency. Seven constraints are handled within 7 s without GPU acceleration. The Pareto front points of nine puncturing tests are obtained in 5 s by using the NSGA-II algorithm. To evaluate the maximum difference and similarity between the planning results and the puncturing points recommended by the physician, Hausdorff distance and overlap rate are respectively developed, the Hausdorff distances are within 30 mm in seven out of nine tests and the average value of overlap rate is 73.0% for all the tests. The puncturing paths of high safety and clinical-practice compliance can be provided by the proposed method, based on which the pretreatment planning software developed can apply to the interns' training and ability evaluating for thermal ablation surgery.

A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks

84%

Li J. , Wu Y. , Shen N. , Zhang J. , Chen E. , Sun J. , Deng Z. , Zhang Y.

Biocybernetics and Biomedical Engineering

2020

tom Vol. 40, no. 1

238--248

The cancer of liver, which is the leading cause of cancer death, is commonly diagnosed by comparing the changes of gray level of liver tissue in the different phases of the patient's CT images. To aid the doctor in reducing misdiagnosis or missed diagnosis, a fully automatic computer-aided diagnosis (CAD) system is proposed to diagnose hepatocellular carcinoma (HCC) using convolutional neural network (CNN) classifier. The automatic segmentation and classification are two core technologies of the proposed CAD system, which are both realized based on CNN. The segmentation of liver and tumor is implemented by a fully convolutional networks (FCN) based on a fine tuning VGG-16 model with two additional 'skip structures' using a weighted loss function which helps to solve the problem of inaccurate tumor segmentation caused by the inevitably unbalanced training data. HCC classification is implemented by a 9-layer CNN classifier, whose input is a 4-channel image data constructed by combining the segmentation result of FCN with the original CT image. A total of 165 venous phase CT images including 46 diffuse tumors, 43 nodular tumors, and 76 massive tumors are used to evaluate the performance of the proposed CAD system. The classification accuracy of CNN classifier for diffuse, nodular and massive tumors are 98.4%, 99.7% and 98.7% respectively, which are significantly improved in contrast with the traditional feature-based ANN and SVM classifiers. The proposed CAD system, which is unaffected by the difference of preprocessing method and feature type, is proved satisfactory and feasible by the test set.