Combined MR brain segmentation

The paper presents a new approach to segmentation of brain from the MR studies. The method is fully automated, very efficient, and quick. The main point of this algorithm is subtraction of T1 series form T2 series (therefore we called it combined), followed by a few image processing steps.The method has been tested using the data sets from three sources. The results were compared numerically to those produced by experts. They indicate great effectiveness of the presented algorithm

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET

The accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results. (c) Springer Nature Switzerland AG 2019

Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis

This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. Both of these factors are clinically relevant for the assessment of multiple sclerosis patients. This work aims to compare the ability of different voxel- and lesion-scale uncertainty measures to capture errors related to segmentation and lesion detection, respectively. Our main contributions are (i) proposing new measures of lesion-scale uncertainty that do not utilise voxel-scale uncertainties; (ii) extending an error retention curves analysis framework for evaluation of lesion-scale uncertainty measures. Our results obtained on the multi-center testing set of 58 patients demonstrate that the proposed lesion-scale measure achieves the best performance among the analysed measures. All code implementations are provided at https://github.com/NataliiaMolch/MS_WML_uncsComment: 4 pages, 2 figures, 3 tables, ISBI preprin

Hybrid-Fusion Transformer for Multisequence MRI

Medical segmentation has grown exponentially through the advent of a fully convolutional network (FCN), and we have now reached a turning point through the success of Transformer. However, the different characteristics of the modality have not been fully integrated into Transformer for medical segmentation. In this work, we propose the novel hybrid fusion Transformer (HFTrans) for multisequence MRI image segmentation. We take advantage of the differences among multimodal MRI sequences and utilize the Transformer layers to integrate the features extracted from each modality as well as the features of the early fused modalities. We validate the effectiveness of our hybrid-fusion method in three-dimensional (3D) medical segmentation. Experiments on two public datasets, BraTS2020 and MRBrainS18, show that the proposed method outperforms previous state-of-the-art methods on the task of brain tumor segmentation and brain structure segmentation.Comment: 10 pages, 4 figure

T1- Weighted MRI Image Segmentation

Growing evidence in recent years indicates that interest in the development of automated image analysis techniques for medical imaging, especially with regard to the discipline of magnetic resonance imaging. T1-weighted MRI scans are often used for both diagnosis and monitoring various neurological disorders, making accurate segmentation of these images crucial for effective treatment planning. In this work, we offer a new method for T1-weighted MRI image segmentation using patch densenet, an image segmentation-specific deep learning architecture. Our method aims to improve the accuracy and efficiency of segmentation, while also addressing some of the challenges associated with traditional segmentation methods. Traditional segmentation methods typically rely on features that are handcrafted and may struggle to accurately capture the intricate details present in MRI images. By utilizing patch densenet, our method automatically learn and extract relevant features from the T1-weighted MRI images and further enhance the accuracy and specificity of the segmentation results. Ultimately, we believe that our proposed approach can greatly improve diagnosis and treatment planning process for neurological disorders

A fuzzy approach for feature extraction of brain tissues in Non-Contrast CT

In neuroimaging, brain tissue segmentation is a fundamental part of the techniques that seek to automate the detection of pathologies, the quantification of tissues or the evaluation of the progress of a treatment. Because of its wide availability, lower cost than other imaging techniques, fast execution and proven efficacy, Non-contrast Cerebral Computerized Tomography (NCCT) is the most used technique in emergency room for neuroradiology examination, however, most research on brain segmentation focuses on MRI due to the inherent difficulty of brain tissue segmentation in NCCT. In this work, three brain tissues were characterized: white matter, gray matter and cerebrospinal fluid in NCCT images. Feature extraction of these structures was made based on the radiological attenuation index denoted by the Hounsfield Units using fuzzy logic techniques. We evaluated the classification of each tissue in NCCT images and quantified the feature extraction technique in images from real tissues with a sensitivity of 92% and a specificity of 96% for images from cases with slice thickness of 1 mm, and 96% and 98% respectively for those of 1.5 mm, demonstrating the ability of the method as feature extractor of brain tissues.Postprint (published version