In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

/pdf/the-multimodal-brain-tumor-image-segmentation-benchmark-2gfraj98dk.pdf

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

The Visual Display of Quantitative Information

With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States1. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20–43% and is now included in US screening guidelines1–6. Existing challenges include inter-grader variability and high false-positive and false-negative rates7–10. We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide. A convolutional neural network performs automated prediction of malignancy risk of pulmonary nodules in chest CT scan volumes and improves accuracy of lung cancer screening.

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.

Size-Based Rendering for Lung Cancer Screening

An processor (118) includes a bullous emphysema identifier (206) that processes voxels of the volumetric image data and identifies voxels corresponding to bullous emphysema, a two dimensional projection image generator (206) that generates a 2D bullous emphysema projection image based on the voxels corresponding to bullous emphysema, wherein an intensity of a contour of a bulla in the 2D bullous emphysema projection image is based on a size of the bulla, and a feature highlighter (210) that highlights the bullous emphysema in the 2D bullous emphysema projection image using second first indicia.

Extracting bullous emphysema and diffuse emphysema in E.G. CT volume images of the lungs

In the context of dynamic contrast enhanced breast MR imaging we analyzed the effect of motion compensation by registration on the characterization of lesions. Two different registration techniques were applied: (1) rigid registration and (2) elastic registration based on the Navier-Lame equation is applied. Interpreting voxels that exhibit a decline in image intensity after contrast injection (compared to the uncontrasted native image) as motion outliers, it can be shown that the rate of motion outliers can be largely reduced by both rigid and elastic registration. The performance of four lesion features including maximal signal enhancement ratio and variance of the signal enhancement ratio measured by area under the ROC curve as well as Cohen's κ value improved for elastic registration only, whereas features derived from rigidly registered images did not reach the performance level of either unregistered or elastically registered data.

The impact of motion correction on lesion characterization in DCE breast MR images

FIELD: physics.SUBSTANCE: invention relates to the computer equipment. Method for visual display of image data comprises: obtaining contrast image data having multiple voxels, wherein each voxel has an intensity value; determining the voxel belonging probability to the vessel; determining the hypoxicity of each voxel; weighing the intensity by a corresponding probability of belonging to the vessel; weighing hypensitivity to a corresponding probability of belonging to a vessel; combining the weighted intensity values and the weighted hypersensitivity values with generating summary image data; and visual display of image summary data.EFFECT: technical result is improved visualization of tissue of interest in contrast image data.18 cl, 15 dwg

Visualizing tissue of interest in contrast image data

The present invention relates to an image processing device for detecting line structures in an image data set. The device comprises a model definition unit (12) for defining a line model of a line structure to be detected, said line model comprising a number of voxels, a calculation unit (14) for calculating, per voxel of interest of said image data set, several correlation values of a correlation between said line model and an image area around said voxel of interest, said image area comprising a corresponding number of voxels as said line model, wherein for each of a number of different relative orientations of said line model with respect to said image area a respective correlation value is calculated, and a determining unit (16) for determining, per voxel of interest, the maximum correlation value from said calculated correlation values and the corresponding optimal orientation at which said maximum correlation value is obtained.

Rafael Wiemker

Papers

Size-Based Rendering for Lung Cancer Screening

Extracting bullous emphysema and diffuse emphysema in E.G. CT volume images of the lungs

The impact of motion correction on lesion characterization in DCE breast MR images

Visualizing tissue of interest in contrast image data

Image processing device and method for detecting line structures in an image data set