Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT

Wang, Jinke; Zu, Hongliang; Guo, Haoyan; Bi, Rongrong; Cheng, Yuanzhi; Tamura, Shinichi

doi:10.1080/24699322.2019.1649076

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Aging brain atlases are built to visualize the variability of brain organs in both adults and infants populations [12][13][14]. Apart from looking into the anatomical characteristics, a single atlas reference is chosen to perform segmentation with unsupervised settings [15,16]. Also, multiple atlas references are randomly picked and registration is performed between the subject moving scans and the multiple atlases' platform [17].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised registration refinement for generating unbiased eye atlas

Lee

Tang

Bao³

et al. 2023

Medical Imaging 2023: Image Processing

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With the confounding effects of demographics across large-scale imaging surveys, substantial variation is demonstrated with the volumetric structure of orbit and eye anthropometry. Such variability increases the level of difficulty to localize the anatomical features of the eye organs for populational analysis. To adapt the variability of eye organs with stable registration transfer, we propose an unbiased eye atlas template followed by a hierarchical coarse-to-fine approach to provide generalized eye organ context across populations. Furthermore, we retrieved volumetric scans from 1842 healthy patients for generating an eye atlas template with minimal biases. Briefly, we select 20 subject scans and use an iterative approach to generate an initial unbiased template. We then perform metric-based registration to the remaining samples with the unbiased template and generate coarse registered outputs. The coarse registered outputs are further leveraged to train a deep probabilistic network, which aims to refine the organ deformation in unsupervised setting. Computed tomography (CT) scans of 100 de-identified subjects are used to generate and evaluate the unbiased atlas template with the hierarchical pipeline. The refined registration shows the stable transfer of the eye organs, which were well-localized in the high-resolution (0.5 𝑚𝑚 ! ) atlas space and demonstrated a significant improvement of 2.37% Dice for inverse label transfer performance. The subject-wise qualitative representations with surface rendering successfully demonstrate the transfer details of the organ context and showed the applicability of generalizing the morphological variation across patients.

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Section: Introductionmentioning

confidence: 99%

Unsupervised registration refinement for generating unbiased eye atlas

Lee

Tang

Bao³

et al. 2023

Medical Imaging 2023: Image Processing

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“…Many traditional liver segmentation methods 7–9,10–14 have been proposed and can be broadly classified into grayscale‐based methods, 7–9,10 structure‐based methods, 11,12 and clustering classification methods 10,11 . The grayscale‐based methods broadly include the region growing method, graph cut method, 7,8 active contour method, 9,10 and threshold‐based method. For example, Yang et al 10 presented a liver segmentation method based on a level set and sparse shape composition.…”

Section: Introductionmentioning

confidence: 99%

Hybrid‐attention densely connected U‐Net with GAP for extracting livers from CT volumes

Chen

Wang

et al. 2022

Medical Physics

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Purpose: Liver segmentation is an important step in the clinical treatment of liver cancer, and accurate and automatic liver segmentation methods are extremely important. U-Net has been used as the benchmark for many medical segmentation networks, but it cannot fully utilize low-resolution information and global contextual information. To solve these problems, we propose a new network architecture named the hybrid-attention densely connected U-Net (HDU-Net). Methods: The proposed HDU-Net has three main changes relative to U-Net, as follows: (1) It uses a densely connected structure and dilated convolution to achieve feature reuse and avoid information loss. (2) A global average pooling block is proposed to further augment the receptive field and improve the segmentation accuracy of the network for small or disconnected liver regions. (3) By combining the spatial attention and channel attention mechanisms, a hybrid attention structure is proposed to replace the skip connection component to filter and integrate low-resolution information. Results: Experiments conducted on the LITS2017, 3Dircadb and Sliver07 datasets show that the proposed model can segment the liver accurately and effectively. Dice scores reach 96.5%, 96.18%, and 97.57% on these datasets, respectively, constituting results that are superior to many previously proposed methods. Conclusions:The experimental liver segmentation results have demonstrated that our proposed network provides improved segmentation performance in comparison with other networks. The experimental results without postprocessing confirmed that our network solves the oversegmentation and undersegmentation problems to some extent. The proposed model is effective, robust, and efficient in terms of liver segmentation without requiring extensive training time or a very large dataset.

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“…The liver segmentation algorithms are mainly classified into the following categories, including regional growth [4], graph cut [5], level set [6], and deep learning [7]. Typically, the deep learning method have become the hot research topics recently as a result of the accumulated data and the increased computing power, especially in machine vision tasks, such as the image classification [8] and segmentation [9].…”

Section: Introductionmentioning

confidence: 99%

DCSegNet: Deep Learning Framework Based on Divide-and-Conquer Method for Liver Segmentation

Yao

et al. 2020

IEEE Access

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Image segmentation plays a vital role in the medical diagnosis and intervention field. The segmentation methods can be classified as fully automated, semiautomated or manual. Among them, manual segmentation can best improve the quality of the results, but it is time-consuming and tedious, and it may lead to operator bias. A continuity-aware probabilistic network based on the divide-and-conquer method was proposed in the current work. The proposed network comprised backbone network, local segmentation and a weight network. The backbone network extracts the features from image. The local segmentation divides the data space, whereas the weight network provides the continuity-aware weights. Therefore, combining those results of the weighted segments can eventually yield precise estimations. In this study, the proposed model was evaluated against several recent methods on the three datasets, and a several performance indexes of segmentation were evaluated for liver segmentation, the results showing that it is the most advanced liver segmentation approach. The source code of this work is publicly shared at https://github.com/licongsheng/DCSegNet for others to easily reproduce the work and build their own models with the introduced mechanisms.

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Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT

Cited by 7 publications

References 20 publications

Unsupervised registration refinement for generating unbiased eye atlas

Unsupervised registration refinement for generating unbiased eye atlas

Hybrid‐attention densely connected U‐Net with GAP for extracting livers from CT volumes

DCSegNet: Deep Learning Framework Based on Divide-and-Conquer Method for Liver Segmentation

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