Phase congruency map driven brain tumour segmentation

Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention. This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively. 12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of p < 0.01 after correction. Combining multiscale texture features lead to a better predictive capacity compared to prediction models based on individual scale features with an average (±SD) classification accuracy of 93.33 (±3.52)%, sensitivity of 88.33 (± 4.12)%, and specificity of 96.89 (± 3.88)%. Entropy was found to be the best classifier feature across all the PT grades with an average of the area under the curve (AUC) value of 91.17, 94.21, 97.70, 100% for ST, BH, IN, and CA, respectively. Our results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade.

Accurate and automated brain tumor segmentation using multi modal MR images is essential for the evaluation of the disease progression in order to improve disease diagnosis and treatment planning. We present a new fully automated method for high graded brain tumor segmentation combining sparse autoencoder and multimodal Fuzzy C-means clustering. The approach utilizes multimodal MRI contrast: T1, T2, FLAIR and T1c (contrast-enhanced) for 15 high graded glioma (HGG) subjects. The objective of the proposed study is to segment tumor tissues from HGG including edema and tumor core within edema. The segmentation was performed on the training data of the multimodal brain tumor image segmentation benchmark 2015. Sparse autoencoder, which is an unsupervised learning algorithm, was used to automatically learn features from unlabeled dataset of tumor in order to segment edema. Followed by edema segmentation, tumor core was segmented from edema using multimodal FCM clustering. Evaluating the performance of the segmentation results with the ground truth yields high dice score (DS) of 0.9866±0.01 and 0.9843±0.01 for edema and tumor core respectively and high Jaccard similarity (JS) of 0.9738±0.02 and 0.9692±0.02 for edema and tumor core respectively; showed high accuracy in segmenting the complex tumor structures from multi-contrast MR scans of HGG patients. We also compared our methodology in terms of segmentation efficiency with some recent techniques reported in proceedings of MICCAI-BRATS challenge 2015.

Gliomas are the most frequent primary brain tumors in adults. Improved quantification of the various aspects of a glioma requires accurate segmentation of the tumor in magnetic resonance images (MRI). Since the manual segmentation is time-consuming and subject to human error and irreproducibility, automatic segmentation has received a lot of attention in recent years. This paper presents a fully automated segmentation method which is capable of automatic segmentation of brain tumor from multi-modal MRI scans. The proposed method is comprised of a deeply-supervised neural network based on Holistically-Nested Edge Detection (HED) network. The HED method, which is originally developed for the binary classification task of image edge detection, is extended for multiple-class segmentation. The classes of interest include the whole tumor, tumor core, and enhancing tumor. The dataset provided by 2017 Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) challenge is used in this work for training the neural network and performance evaluations. Experiments on BraTS 2017 challenge datasets demonstrate that the method performs well compared to the existing works. The assessments revealed the Dice scores of 0.86, 0.60, and 0.69 for whole tumor, tumor core, and enhancing tumor classes, respectively.

Gliomas are the most prevalent and aggressive primary brain tumors characterized by rapid progressions and infiltration. Timely and precise diagnosis is crucial for effective oncology treatment. Magnetic Resonance Imaging (MRI) facilitates noninvasive assessment of brain lesions. Manual brain tumor evaluation from MRI scans is labor-intensive, relies heavily on clinician experience, and is prone to errors. Consequently, automated diagnosis of brain tumors is essential for optimal clinical management and glioma surgical interventions. This study introduces an Integrated Deep Convolutional Neural Network (IDCNN)-based framework for segmenting and grading glioma tumors from multimodal MRI scans. The framework integrates two state-of-the-art CNN architectures. Based on 3D U-Net architecture, the first CNN performs tumor segmentation from multimodal MRI volumes. The segmentation network utilizes the encoder for feature extraction and dimensionality reduction, while the decoder reconstructs the output to the original input size. A thorough performance evaluation of CNN architectures based on pre-trained (ResNet50, EfficientNetB0, DensNet121, EfficientNetB2, and ResNet152V2) was conducted on a three-class dataset of glioma, meningioma, and pituitary tumors to determine the best model for tumor grading. EfficientNetB2 surpassed other models across all evaluation metrics, achieving 99.19% test accuracy, 99.17% precision, 98.94% sensitivity, 99.57% specificity, and 99.06% F1-score. The optimal EfficientNetB2 model was subsequently utilized to grade tumors identified by the segmentation network. The proposed framework exhibited exceptional performance in both segmentation and grading tasks, attaining dice coefficient scores (DSC) of 86.13, 86.75, and 92.41 in enhancing tumor, tumor core, and whole tumor, respectively, alongside 98.49% classification accuracy for high- and low-grade glioma. Experimental findings validate the superior capabilities of the proposed framework compared to the existing methods. These results highlight the potential of the proposed model to aid radiologists in achieving accurate and reliable diagnoses, improving patient outcomes, and supporting clinical decision-making.

Gliomas are the most prevalent and aggressive primary brain tumors characterized by rapid progressions and infiltration. Timely and precise diagnosis is crucial for effective oncology treatment. Magnetic Resonance Imaging (MRI) facilitates noninvasive assessment of brain lesions. Manual brain tumor evaluation from MRI scans is labor-intensive, relies heavily on clinician experience, and is prone to errors. Consequently, automated diagnosis of brain tumors is essential for optimal clinical management and glioma surgical interventions. This study introduces an Integrated Deep Convolutional Neural Network (IDCNN)-based framework for segmenting and grading glioma tumors from multimodal MRI scans. The framework integrates two state-of-the-art CNN architectures. Based on 3D U-Net architecture, the first CNN performs tumor segmentation from multimodal MRI volumes. The segmentation network utilizes the encoder for feature extraction and dimensionality reduction, while the decoder reconstructs the output to the original input size. A thorough performance evaluation of CNN architectures based on pre-trained (ResNet50, EfficientNetB0, DensNet121, EfficientNetB2, and ResNet152V2) was conducted on a three-class dataset of glioma, meningioma, and pituitary tumors to determine the best model for tumor grading. EfficientNetB2 surpassed other models across all evaluation metrics, achieving 99.19% test accuracy, 99.17% precision, 98.94% sensitivity, 99.57% specificity, and 99.06% F1-score. The optimal EfficientNetB2 model was subsequently utilized to grade tumors identified by the segmentation network. The proposed framework exhibited exceptional performance in both segmentation and grading tasks, attaining dice coefficient scores (DSC) of 86.13, 86.75, and 92.41 in enhancing tumor, tumor core, and whole tumor, respectively, alongside 98.49% classification accuracy for high- and low-grade glioma. Experimental findings validate the superior capabilities of the proposed framework compared to the existing methods. These results highlight the potential of the proposed model to aid radiologists in achieving accurate and reliable diagnoses, improving patient outcomes, and supporting clinical decision-making.

A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core. The cascade is designed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step. The enhancing tumor core is then segmented based on the bounding box of the tumor core segmentation result. Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives. Residual connections and multi-scale predictions are employed in these networks to boost the segmentation performance. Experiments with BraTS 2017 validation set show that the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for enhancing tumor core, whole tumor and tumor core, respectively. The corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and 0.7748, respectively.

One of the fundamental challenges in basic neuroscience is to understand the molecular genetic networks associated with building the brain. As malfunction in these genetic pathways can lead to disorders like cancer, brain development is also a crucial research area for clinical neuroscience. In the course of this thesis, different molecular aspects of Drosophila brain development and related neoplastic disease were analyzed using high-density oligonucleotide arrays. The homeotic selector gene labial (lab) plays an important role in specification of neuronal identity in the embryonic brain of Drosophila. In labial mutants presumptive neurons in the posterior tritocerebrum fail to differentiate. This leads to severe defects in tritocerebral axon pathways. Using high density oligonucleotide arrays we identified downstream target genes of Labial and showed that only a limited and distinct set of genes expressed in the embryo is regulated by this homeoprotein. Furthermore, we performed genetic rescue experiments to analyze the functional equivalence of Drosophila Hox gene products in specification of the tritocerebral neuromere. Surprisingly, all tested homeotic proteins, with the exception of Abd-B, were able to rescue the labial mutant phenotype in the tritocerebrum. These results indicate that the specificity of homeotic gene action in embryonic brain development has to be modulated by cis-acting regulatory elements. Another study circled around the homeobox transcription factor otd and its human homolog Otx2. Cross-phylum rescue experiments have shown that these genes are functionally equivalent. We used quantitative transcript imaging to analyze otd and Otx gene action in the Drosophila embryo at a genomic level. Our experiments suggest that about one third of the Otd-regulated transcripts in Drosophila can also be controlled by the human Otx2. These common otd/Otx2 downstream genes are likely to represent the molecular basis for the functional equivalence of otd and Otx2 gene action in Drosophila. glial cells missing (gcm) is a key control gene of gliogenesis. gcm loss-of-function leads to a transformation of glial cells into neurons and, conversely, when gcm is ectopically misexpressed, presumptive neurons become glia. Since gcm encodes a transcription factor it is supposed that a set of downstream genes are regulated by GCM that in turn execute the glial differentiation program. Again, a set of full-genome transcript profiling experiments was conducted to identify gcm downstream genes in a comprehensive manner. A set of several hundred candidate gcm target genes were identified in this screen, giving new insights into neuroglial fate specification in Drosophila. Brain tumors have been extensively studied by looking at genetic alterations and mutations that lead to malignant growth. Still, the causes of brain tumorigenesis are largely unknown. Model systems like Drosophila can be of great help to shed light on altered transcriptional activity in brain tumor phenotypes. To investigate the in vivo transcriptional activity associated with a brain tumor, we conducted genome-wide microarray expression analyses of an adult brain tumor in Drosophila caused by homozygous mutation in the tumor suppressor gene brain tumor (brat). Two independent gene expression studies using two different oligonucleotide microarray platforms were used to compare the transcriptome of adult wildtype flies with mutants displaying the adult bratk06028 mutant brain tumor. Cross-validation and stringent statistical criteria identified a core transcriptional signature of bratk06028 neoplastic tissue. We found highly significant expression level changes for 321 annotated genes associated with the adult neoplastic bratk06028 tissue indicating elevated and aberrant metabolic and cell cycle activity, upregulation of the basal transcriptional machinery, as well as elevated and aberrant activity of ribosome synthesis and translation control. One fifth of these genes show homology to known mammalian genes involved in cancer formation. These results identify for the first time the genome-wide transcriptional alterations associated with an adult brain tumor in Drosophila and reveal insights into the possible mechanisms of tumor formation caused by homozygous mutation of the translational repressor brat.

To address the challenges of manual inspection dependency, low efficiency, and high costs in evaluating the surface grinding quality of composite materials, this study investigated machine vision-based surface recognition algorithms. We proposed a multi-scale texture fusion analysis algorithm that innovatively integrated luminance analysis with multi-scale texture features through decision-level fusion. Specifically, a modified Rayleigh parameter was developed during luminance analysis to rapidly pre-segment unpolished areas by characterizing surface reflection properties. Furthermore, we enhanced the traditional Otsu algorithm by incorporating global grayscale mean (μ) and standard deviation (σ), overcoming its inherent limitations of exclusive reliance on grayscale histograms and lack of multimodal feature integration. This optimization enables simultaneous detection of specular reflection defects and texture uniformity variations. To improve detection window adaptability across heterogeneous surface regions, we designed a multi-scale texture analysis framework operating at multiple resolutions. Through decision-level fusion of luminance analysis and multi-scale texture evaluation, the proposed algorithm achieved 96% recognition accuracy with >95% reliability, demonstrating robust performance for automated surface grinding quality assessment of composite materials.

Quantitative tumour growth into the brain (stage of the disease) and qualitative tumour evolution in the time (progression) are the two basic aspects of the natural history of the cerebral neoplastic disease. Recently the development of neuroradiological imaging (CT and MR) and the progress in biopathology of the nervous system tumours introduced new concepts like heterogeneity of neuroepithelial tumours or evolution to anaplasia. The findings obtained in 159 neuroepithelial tumours studied with stereotactic biopsy from 1980 to 1987 are presented. Most of them were glioblastomas (n = 43; 27%) and astrocytic tumours (n = 81; 50.9%). Twenty-nine cases of fibrillary astrocytomas (35.8% of all astrocytic tumours) showed focal anaplasia (progression). In 10 out of the 43 glioblastomas (23.3%) signs of astrocytic differentiation were clearly evident (secondary glioblastoma?). Our data confirm that neuroectodermal tumours, particularly of astrocytic series, undergo progression through anaplasia, which may be at the beginning a circumscribed phenomenon (focal anaplasia). The staging of the disease (tumour growth) into cerebral nervous system in some cases can not be correctly expressed through the neuroradiological imaging. Sometime CT scan may underestimate the true extension of the lesion. On the contrary, MR may overrate the extension of the lesion. Such mistakes in evaluation of tumour staging may be corrected through seriate stereotactic biopsy.

Automated segmentation of brain tumour from multimodal MR images is pivotal for the analysis and monitoring of disease progression. As gliomas are malignant and heterogeneous, efficient and accurate segmentation techniques are used for the successful delineation of tumours into intra-tumoural classes. Deep learning algorithms outperform on tasks of semantic segmentation as opposed to the more conventional, context-based computer vision approaches. Extensively used for biomedical image segmentation, Convolutional Neural Networks have significantly improved the state-of-the-art accuracy on the task of brain tumour segmentation. In this paper, we propose an ensemble of two segmentation networks: a 3D CNN and a U-Net, in a significant yet straightforward combinative technique that results in better and accurate predictions. Both models were trained separately on the BraTS-19 challenge dataset and evaluated to yield segmentation maps which considerably differed from each other in terms of segmented tumour sub-regions and were ensembled variably to achieve the final prediction. The suggested ensemble achieved dice scores of 0.750, 0.906 and 0.846 for enhancing tumour, whole tumour, and tumour core, respectively, on the validation set, performing favourably in comparison to the state-of-the-art architectures currently available.

Many deep learning methods have been proposed for brain tumor segmentation from multi-modal Magnetic Resonance Imaging (MRI) scans that are important for accurate diagnosis and treatment planning. However, supervised learning needs a large amount of labeled data to perform well, where the time-consuming and expensive annotation process or small size of training set will limit the model's performance. To deal with these problems, self-supervised pre-training is an appealing solution due to its feature learning ability from a set of unlabeled images that is transferable to downstream datasets with a small size. However, existing methods often overlook the utilization of multi-modal information and multi-scale features. Therefore, we propose a novel Self-Supervised Learning (SSL) framework that fully leverages multi-modal MRI scans to extract modality-invariant features for brain tumor segmentation. First, we employ a Siamese Block-wise Modality Masking (SiaBloMM) strategy that creates more diverse model inputs for image restoration to simultaneously learn contextual and modality-invariant features. Meanwhile, we proposed Overlapping Random Modality Sampling (ORMS) to sample voxel pairs with multi-scale features for self-distillation, enhancing voxel-wise representation which is important for segmentation tasks. Experiments on the BraTS 2024 adult glioma segmentation dataset showed that with a small amount of labeled data for fine-tuning, our method improved the average Dice by 3.80 percentage points. In addition, when transferred to three other small downstream datasets with brain tumors from different patient groups, our method also improved the dice by 3.47 percentage points on average, and outperformed several existing SSL methods. The code is availiable at https://github.com/HiLab-git/Vox-MMSD.

Accurate brain tumor segmentation is crucial for clinical assessment, follow-up, and subsequent treatment of gliomas. While convolutional neural networks (CNN) have become state of the art in this task, most proposed models either use 2D architectures ignoring 3D contextual information or 3D models requiring large memory capacity and extensive learning databases. In this study, an ensemble of two kinds of U-Net-like models based on both 3D and 2.5D convolutions is proposed to segment multimodal magnetic resonance images (MRI). The 3D model uses concatenated data in a modified U-Net architecture. In contrast, the 2.5D model is based on a multi-input strategy to extract low-level features from each modality independently and on a new 2.5D Multi-View Inception block that aims to merge features from different views of a 3D image aggregating multi-scale features. The Asymmetric Ensemble of Asymmetric U-Net (AE AU-Net) based on both is designed to find a balance between increasing multi-scale and 3D contextual information extraction and keeping memory consumption low. Experiments on 2019 dataset show that our model improves enhancing tumor sub-region segmentation. Overall, performance is comparable with state-of-the-art results, although with less learning data or memory requirements. In addition, we provide voxel-wise and structure-wise uncertainties of the segmentation results, and we have established qualitative and quantitative relationships between uncertainty and prediction errors. Dice similarity coefficient for the whole tumor, tumor core, and tumor enhancing regions on BraTS 2019 validation dataset were 0.902, 0.815, and 0.773. We also applied our method in BraTS 2018 with corresponding Dice score values of 0.908, 0.838, and 0.800.

We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connectivity patterns to reduce the number of weights and residual connection and is initialized with weights obtained from training this model with BraTS 2018 dataset. Hard mining is done during training to train for the difficult cases of segmentation tasks by increasing the dice similarity coefficient (DSC) threshold to choose the hard cases as epoch increases. On the BraTS2020 validation data (n = 125), this architecture achieved a tumor core, whole tumor, and active tumor dice of 0.744, 0.876, 0.714, respectively. On the test dataset, we get an increment in DSC of tumor core and active tumor by approximately 7%. In terms of DSC, our network performances on the BraTS 2020 test data are 0.775, 0.815, and 0.85 for enhancing tumor, tumor core, and whole tumor, respectively. Overall survival of a subject is determined using conventional machine learning from rediomics features obtained using generated segmentation mask. Our approach has achieved 0.448 and 0.452 as the accuracy on the validation and test dataset.KeywordsGliomasMRI3D CNNSegmentationHard miningOverall survival

Neurologic tumors account for over 50% of American acute inpatient rehabilitation facility cancer admissions. WHO Grade IV astrocytoma (also known as glioblastoma multiforme, high grade glioma or GBM) is the most common primary brain tumor in adults and is invariably fatal. The majority of primary brain tumor patients experience neurologic deficits. However, under-referral from oncology to rehabilitation has been reported. This brief narrative review article covers functional, medical and regulatory considerations when rehabilitating brain tumor inpatients.

Artificial intelligence technology is making a growing influence in the healthcare industry. Growth of abnormal cells or lump in your brain is known as a brain tumour. Many different types of brain tumours exist. Primarily brain tumors can be classified into benign (non -cancerous) and malignant (cancerous). According to a random-effects model, the total incidence rate of all brain cancers is 10.82 (95% CI: 8.63-13.56) per 100 000 person-years. The most aggressive and potentially fatal brain tumours, gliomas grow extremely quickly. Due to their uneven shape and diffused borders with the surrounding area, gliomas might be difficult to segment using computer-aided diagnostics. A lot of work is being done to improve the effectiveness of Brain tumour Detection in the early stages. The most popular technique for visualising important brain areas is magnetic resonance imaging (MRI). MRI processing is one of the components of image processing, which has recently experienced the largest growth. The growth Detection is frequently the first stage. This study explains the thresholding method for brain tumour identification. The suggested method can effectively be used to recognise and isolate brain tumours in MRI images. It functions as a useful tool for doctors who practise in this area. In this paper, a deep learning-based technique for segmenting brain tumours from several MRI modalities is given.