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Automatic Liver And Tumor Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

Georgios Kaissis, R. Braren, Bjoern H Menze, Jana Lipková, Seyed-Ahmad Ahmadi, Patrick Bilic, P. Christ, M. Rempfler, Florian Ettlinger, Felix Grün, J. Holch, Felix O. Hofmann, W. Sommer, V. Heinemann, Mohamed Ezzeldin A. Elshaer, Sebastian J. Schlecht, F. Ahmaddy, S. Tatavarty, Marc Bickel, M. D’Anastasi•Sun Feb 19 2017

Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume.

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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Q. Dou, Hao Chen, P. Heng, Xiaojuan Qi, Chi-Wing Fu, X. Li•Sun Jun 10 2018

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

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Robust End-to-End Focal Liver Lesion Detection Using Unregistered Multiphase Computed Tomography Images

Sungroh Yoon, Eunji Kim, Sang-gil Lee, J. S. Bae, J. H. Kim•Wed Dec 01 2021

Evaluation on a large-scale dataset with 280 patients confirmed that the proposed method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images.

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Automatic Liver And Tumor Segmentation | State-of-the-Art