Automatic detection and quantification of brain midline shift using anatomical marker model

Liu, Ruizhe; Li, Shimiao; Su, Bolan; Tan, Chew Lim; Leong, Tze-Yun; Pang, Boon Chuan; Lim, Charles; Lee, Cheng Kiang

doi:10.1016/j.compmedimag.2013.11.001

Cited by 22 publications

(31 citation statements)

References 12 publications

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“…However, in previous publications the segmentation of medical pathologies (like Glioblastoma Multiforme, Pituitary Adenomas, Cerebral Aneurysms, Prostate Central Glands and Vertebral Bodies) have already been evaluated via one fixed user-defined seed point, and the summary of these results have been presented here28. There, it could already show that a DSC around 80% is possible with only one seed point.…”

Section: Resultsmentioning

confidence: 99%

“…The novelty within this study lies in the combination of several pre-developed segmentation techniques262829303132, resulting in an advanced interactive real-time contouring algorithm for (medical) data. More specific, the presented work extends and incorporates a refinement option2930 – introduced only for fixed seed points and a spherical shape31 – into the recently published Interactive-Cut28 algorithm that can handle arbitrary shapes32, but had no refinement option.…”

Section: Discussionmentioning

confidence: 99%

“…More specific, the presented work extends and incorporates a refinement option2930 – introduced only for fixed seed points and a spherical shape31 – into the recently published Interactive-Cut28 algorithm that can handle arbitrary shapes32, but had no refinement option. In sum, the achieved research highlights of the study are:

An novel interactive contouring algorithm has been designed;

The algorithm combines shape-based segmentation with user refinement;

The user refinement is intuitive and fast, with immediate feedback;

The segmentation works on 2D and 3D image data;

The evaluation has been performed on medical data from the clinical routine.

…”

Section: Discussionmentioning

confidence: 99%

“…The core algorithm has been implemented as own MeVisLab C++ module and is a combination and extension of the Template-Cut32 and the Interactive-Cut28 approaches, and the refinement method introduced in2930. The new algorithm (Refinement-Cut), as well as the predecessor methods it builds up, belong to the graph-based approaches.…”

Section: Methodsmentioning

confidence: 99%

“…This template represents the basic shape of the segmented object, like described in the Template-Cut approach. Examples areA rectangle shape for vertebra segmentation in 2D26; A circle template for prostate central gland segmentation in 2D28; A cubic shape for vertebral body segmentation in 3D4647; A spherical shape for prostate central gland or brain tumor segmentation in 3D3143; Or even a user-defined shape for objects that vary too much to be predefined by a simple shape48. …”

Section: Methodsmentioning

confidence: 99%

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Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science

Egger

2014

Sci Rep

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In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. For these difficult cases the algorithm still needs additional user support. However, this additional user support should be intuitive and rapid integrated into the segmentation process, without breaking the interactive real-time segmentation feedback. I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases. These additional seed(s) restrict(s) the calculation of the segmentation for the algorithm, but at the same time, still enable to continue with the interactive real-time feedback segmentation. For a practical and genuine application in translational science, the approach has been tested on medical data from the clinical routine in 2D and 3D.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An novel interactive contouring algorithm has been designed;

The algorithm combines shape-based segmentation with user refinement;

The user refinement is intuitive and fast, with immediate feedback;

The segmentation works on 2D and 3D image data;

The evaluation has been performed on medical data from the clinical routine.

…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science

Egger

2014

Sci Rep

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The delineation of largely deformed brain midline using regression‐based line detection network

et al. 2020

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Purpose The human brain has two cerebral hemispheres that are roughly symmetric and separated by a midline, which is nearly a straight line shown in axial computed tomography (CT) images in healthy subjects. However, brain diseases such as hematoma and tumors often cause midline shift, where the degree of shift can be regarded as a quantitative indication in clinical practice. To facilitate clinical evaluation, we need computer‐aided methods to automate this quantification. Nevertheless, most existing studies focused on the landmark‐ or symmetry‐based methods that provide only the existence of shift or its maximum distance, which could be easily affected by anatomical variability and large brain deformations. Intuitive results such as midline delineation or measurement are lacking. In this study, we focus on developing an automated and robust method based on the fully convolutional neural network for the delineation of midline in largely deformed brains. Methods We propose a novel regression‐based line detection network (RLDN) for the robust midline delineation, especially in largely deformed brains. Specifically, to improve the robustness of delineation in largely deformed brains, we regard the delineation of the midline as the skeleton extraction task and then use the multiscale bidirectional integration module to acquire more representative features. Based on the skeleton extraction, we incorporate the regression task into it to delineate more accurate and continuous midline, especially in largely deformed brains. Our study utilized the public CQ 500 dataset (128 subjects) for training with hold‐out validation on 61 subjects from a private cohort accrued from a local hospital. Results The mean line distance error and F1‐score were 1.17 ± 0.72 mm with 0.78 on CQ 500 test set, and 4.15 ± 3.97 mm with 0.61 on the private dataset. Besides, significant differences (P < 0.05) were observed between our method and other comparative ones on these two datasets. Conclusions This work provides a novel solution to acquire robust delineation of the midline, especially in largely deformed brains, and achieves state‐of‐the‐art performance on the public and our private dataset, which makes it possible for automated diagnosis of relevant brain diseases in the future.

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