Brain MRI Intensity Inhomogeneity Correction Using Region of Interest, Anatomic Structural Map, and Outlier Detection

Abstract Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI.Methods: The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Results: Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (I of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions.Conclusions: The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

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Ensemble classification and segmentation for intracranial metastatic tumors on MRI images based on 2D U-nets

Scientific Reports ◽

10.1038/s41598-021-99984-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Cheng-Chung Li ◽

Meng-Yun Wu ◽

Ying-Chou Sun ◽

Hung-Hsun Chen ◽

Hsiu-Mei Wu ◽

...

Keyword(s):

Active Contour Model ◽

Brain Mri ◽

Gamma Knife Radiosurgery ◽

Imbalanced Data ◽

Brain Magnetic Resonance Imaging ◽

Region Of Interest ◽

Ensemble Classification ◽

Metastatic Tumors ◽

Post Contrast ◽

Magnetic Resonance Imaging Mri

AbstractThe extraction of brain tumor tissues in 3D Brain Magnetic Resonance Imaging (MRI) plays an important role in diagnosis before the gamma knife radiosurgery (GKRS). In this article, the post-contrast T1 whole-brain MRI images had been collected by Taipei Veterans General Hospital (TVGH) and stored in DICOM format (dated from 1999 to 2018). The proposed method starts with the active contour model to get the region of interest (ROI) automatically and enhance the image contrast. The segmentation models are trained by MRI images with tumors to avoid imbalanced data problem under model construction. In order to achieve this objective, a two-step ensemble approach is used to establish such diagnosis, first, classify whether there is any tumor in the image, and second, segment the intracranial metastatic tumors by ensemble neural networks based on 2D U-Net architecture. The ensemble for classification and segmentation simultaneously also improves segmentation accuracy. The result of classification achieves a F1-measure of $$75.64\%$$ 75.64 % , while the result of segmentation achieves an IoU of $$84.83\%$$ 84.83 % and a DICE score of $$86.21\%$$ 86.21 % . Significantly reduce the time for manual labeling from 30 min to 18 s per patient.

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Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI

Technology and Health Care ◽

10.3233/thc-213149 ◽

2021 ◽

pp. 1-12

Author(s):

Lin Wu ◽

Tian He ◽

Jie Yu ◽

Hang Liu ◽

Shuang Zhang ◽

...

Keyword(s):

Bias Field ◽

Correction Method ◽

Surface Coil ◽

Intensity Inhomogeneity ◽

Acquisition Time ◽

Surface Coils ◽

In Vivo Experiments ◽

Inhomogeneity Correction ◽

Volume Coil

BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.

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A diffusion‐based compensation approach for intensity inhomogeneity correction in MRI

International Journal of Imaging Systems and Technology ◽

10.1002/ima.22416 ◽

2020 ◽

Vol 30 (3) ◽

pp. 761-778

Author(s):

Maryjo M. George ◽

S. Kalaivani

Keyword(s):

Intensity Inhomogeneity ◽

Inhomogeneity Correction ◽

Compensation Approach

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A Review on MR Image Intensity Inhomogeneity Correction

International Journal of Biomedical Imaging ◽

10.1155/ijbi/2006/49515 ◽

2006 ◽

Vol 2006 ◽

pp. 1-11 ◽

Cited By ~ 114

Author(s):

Zujun Hou

Keyword(s):

Statistical Models ◽

Low Frequency ◽

Intensity Inhomogeneity ◽

Computer Memory ◽

Mr Image ◽

Cpu Time ◽

Inhomogeneity Correction ◽

Recent Developments ◽

Frequency Components ◽

Overall Performance

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.

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