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MDCF_Net: A Multi-dimensional hybrid network for liver and tumor segmentation from CT

Jiang Jian, Peng Yanjun, Hou Qingfan, Wang Jiao

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 2

494--506

EN

The segmentation of the liver and liver tumors is critical in the diagnosis of liver cancer, and the high mortality rate of liver cancer has made it one of the most popular areas for segmentation research. Some deep learning segmentation methods outperformed traditional methods in terms of segmentation results. However, they are unable to obtain satisfactory segmentation results due to blurred original image boundaries, the presence of noise, very small lesion sites, and other factors. In this paper, we propose MDCF_Net, which has dual encoding branches composed of CNN and CnnFormer and can fully utilize multidimensional image features. First, it extracts both intra-slice and inter-slice information and improves the accuracy of the network output by symmetrically using multidimensional fusion layers. In the meantime, we propose a novel feature map stacking approach that focuses on the correlation of adjacent channels of two feature maps, improving the network’s ability to perceive 3D features. Furthermore, the two coding branches collaborate to obtain both texture and edge features, and the network segmentation performance is further improved. Extensive experiments were carried out on the public datasets LiTS to determine the optimal slice thickness for this task. The superiority of the segmentation performance of our proposed MDCF_Net was confirmed by comparison with other leading methods on two public datasets, the LiTS and the 3DIRCADb.

2

A deep attention network via high-resolution representation for liver and liver tumor segmentation

Li Yang, Zou Beiji, Liu Qing

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1518--1532

EN

The segmentation of liver and liver tumor is an essential step for computer-aided liver disease diagnosis, treatment and prognosis. Although deep convolutional neural networks have contributed to liver and tumor segmentation, their architectures can not maintain spatial details and long-range context information. Besides, the fixed receptive fields of these networks limit the segmentation performance of livers and tumors with variant sizes and shapes. To address above problems, we propose a deep attention neural network which contains high-resolution branch and multi-scale features aggregation for cascaded liver and tumor segmentation from CT images. To be specific, the high-resolution branch can maintain the resolution of the input image and thus preserves the spatial details. The multi-scale features exchange and fusion enable the receptive fields of the network to adapt to liver and tumor with variant shapes and sizes. The appended attention module evaluates the similarities between every two pixels to model the long-range dependence and context information so that the network can segment liver and tumor areas located in distant regions. Experimental results on the LiTS and the 3D-IRCADb datasets demonstrate that our method can generate satisfying performance.

3

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

Li Jing, Xu Yuanqi, Shen Nanyan, Feng Lanyun, Ran Zhuang, Deng Zongqian

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1469--1485

EN

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.

4

Wykorzystanie właściwości elektrycznych komórek nowotworowych do monitorowania termoablacji

Szuflitowska B.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

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2015

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Vol. 21, nr 2

86--90

PL

Badania opisane w pracy dotyczą konduktywności i przenikalności elektrycznej tkanek w aspekcie termoablacji prądem wysokiej częstotliwości. Ablacja elektryczna jest jedną z metod leczenia nieresekcyjnych guzów wątroby. Wzrost konduktywności jest zwykle skorelowany z typem nowotworu i jego stadium. W artykule zaprezentowano symulację zabiegu w zależności od zastosowanego napięcia i wartości konduktywności tkanek nowotworowych.

EN

The influence of the electrical conductivity and dielectric permittivity of biological tissues on the radiofrequency termoablation outcomes is discussed. Increase of conductivity correlates with the tumor type and stage. Radiofrequency thermal ablation is one of the methods used for the treatment of unresectable liver tumors. In this paper, the simulation of the ablation process depending of the applied voltage and the tumor conductivity, is presented.