Stereotactic neuro‐radiosurgery is a well‐established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft‐tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and growth. Therefore, particular attention must be deserved to these hypoxic areas that could lead to recurrent cancers and resistance to therapeutic damage. This article proposes a novel fully automatic method for necrosis extraction (NeXt), using the Fuzzy C‐Means algorithm, after the GTV segmentation. This unsupervised Machine Learning technique detects and delineates the necrotic regions also in heterogeneous cancers. The overall processing pipeline is an integrated two‐stage segmentation approach useful to support neuro‐radiosurgery. NeXt can be exploited for dose escalation, allowing for a more selective strategy to increase radiation dose in hypoxic radioresistant areas. Moreover, NeXt analyzes contrast‐enhanced T1‐weighted MR images alone and does not require multispectral MRI data, representing a clinically feasible solution. This study considers an MRI dataset composed of 32 brain metastatic cancers, wherein 20 tumors present necroses. The segmentation accuracy of NeXt was evaluated using both spatial overlap‐based and distance‐based metrics, achieving these average values: Dice similarity coefficient 95.93% ± 4.23% and mean absolute distance 0.225 ± 0.229 (pixels). 相似文献
In this work, a simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI). Poor contrast MR images are preprocessed by using morphological operations and DSR (dynamic stochastic resonance) technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest (ROI) lies in the middle and its size ranges from 240 × 240 mm2 to 280 × 280 mm2. The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block‐based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐means, fuzzy c‐means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work. 相似文献
Surgical resection is a mainstay in the treatment of malignant brain tumors. Surgeons, however, face great challenges in distinguishing tumor margins due to their infiltrated nature. Here, a pair of gold nanoprobes that enter a brain tumor by crossing the blood–brain barrier is developed. The acidic tumor environment triggers their assembly with the concomitant activation of both magnetic resonance (MR) and surface‐enhanced resonance Raman spectroscopy (SERRS) signals. While the bulky aggregates continuously trap into the tumor interstitium, the intact nanoprobes in normal brain tissue can be transported back into the blood stream in a timely manner. Experimental results show that physiological acidity triggers nanoparticle assembly by forming 3D spherical nanoclusters with remarkable MR and SERRS signal enhancements. The nanoprobes not only preoperatively define orthotopic glioblastoma xenografts by magnetic resonance imaging (MRI) with high sensitivity and durability in vivo, but also intraoperatively guide tumor excision with the assistance of a handheld Raman scanner. Microscopy studies verify the precisely demarcated tumor margin marked by the assembled nanoprobes. Taking advantage of the nanoprobes' rapid excretion rate and the extracellular acidification as a hallmark of solid tumors, these nanoprobes are promising in improving brain‐tumor surgical outcome with high specificity, safety, and universality. 相似文献
Magnetic resonance imaging (MRI) brain image segmentation is essential at preliminary stage in the neuroscience research and computer‐aided diagnosis. However, presence of noise and intensity inhomogeneity in MRI brain images leads to improper segmentation. The fuzzy entropy clustering (FEC) is often used to deal with noisy data. One major disadvantage of the FEC algorithm is that it does not consider the local spatial information. In this article, we have proposed an improved fuzzy entropy clustering (IFEC) algorithm by introducing a new fuzzy factor, which incorporates both local spatial and gray‐level information. The IFEC algorithm is insensitive to noise, preserves the image detail during clustering, and is free of parameter selection. The efficacy of IFEC algorithm is demonstrated by comparing it quantitatively with the state‐of‐the‐art segmentation approaches in terms of similarity index on publically available real and simulated MRI brain images. 相似文献
Brain gliomas, common in adults, pose significant diagnostic challenges. Accurate segmentation from multimodal magnetic resonance imaging (MRI) scans is critical for effective treatment planning. Traditional manual segmentation methods, labor-intensive and error-prone, often lead to inconsistent diagnoses. To overcome these limitations, our study presents a sophisticated framework for the automated segmentation of brain gliomas from multimodal MRI images. This framework consists of three integral components: a 3D UNet, a classifier, and a Classifier Weight Transformer (CWT). The 3D UNet, acting as both an encoder and decoder, is instrumental in extracting comprehensive features from MRI scans. The classifier, employing a streamlined 1 × 1 convolutional architecture, performs detailed pixel-wise classification. The CWT integrates self-attention mechanisms through three linear layers, a multihead attention module, and layer normalization, dynamically refining the classifier's parameters based on the features extracted by the 3D UNet, thereby improving segmentation accuracy. Our model underwent a two-stage training process for maximum efficiency: in the first stage, supervised learning was used to pre-train the encoder and decoder, focusing on robust feature representation. In the second stage, meta-training was applied to the classifier, with the encoder and decoder remaining unchanged, ensuring precise fine-tuning based on the initially developed features. Extensive evaluation of datasets such as BraTS2019, BraTS2020, BraTS2021, and a specialized private dataset (ZZU) underscored the robustness and clinical potential of our framework, highlighting its superiority and competitive advantage over several state-of-the-art approaches across various segmentation metrics in training and validation sets. 相似文献
In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance. 相似文献
The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation. 相似文献
The focus of this paper is to automatically segment and label continuous speech signal into syllable-like units for Indian languages. In this approach, the continuous speech signal is first automatically segmented into syllable-like units using group delay based algorithm. Similar syllable segments are then grouped together using an unsupervised and incremental training (UIT) technique. Isolated style HMM models are generated for each of the clusters during training. During testing, the speech signal is segmented into syllable-like units which are then tested against the HMMs obtained during training. This results in a syllable recognition performance of 42·6% and 39·94% for Tamil and Telugu. A new feature extraction technique that uses features extracted from multiple frame sizes and frame rates during both training and testing is explored for the syllable recognition task. This results in a recognition performance of 48·7% and 45·36%, for Tamil and Telugu respectively. The performance of segmentation followed by labelling is superior to that of a flat start syllable recogniser (27·8%and 28·8%for Tamil and Telugu respectively). 相似文献
Brain magnetic resonance imaging (MRI) is crucial for diagnosing and understanding neurological disorders, but inherent limitations hinder the visualization of fine details in brain structures. The emergence of super-resolution techniques, especially deep-learning methods, has improved imaging quality of MRI, by increasing MRI spatial resolution. At present, deep-learning algorithms mostly performed super-resolution on 2D MRI images. However, considering 3D nature of MRI, 3D models are more suitable for brain MRI super-resolution. To achieve finer brain structural details, this study proposes a 3D brain MRI super-resolution method based on diffusion model (3D-SRDM), which is a fast and easily trainable neural network for the generation of high-resolution brain MRI images. In our 3D-SRDM model, the self-attention module in U-Net is replaced with 3D spatial attention mechanism. The network structure of 3D-SRDM is optimized to reduce training parameters. Moreover, accelerated sampling from denoising diffusion implicit model is also incorporated to reduce time consumption. By these optimizations, compared with original diffusion model, the proposed model can achieve about 10- and 5-fold speed increase at 4× and 8× super-resolution of 3D brain MRI volumes, respectively, almost without affecting image quality. Thus, 3D-SRDM has potential application value in efficiently generating high-resolution 3D brain MRI images, thus facilitating the doctors' diagnosis. 相似文献
Abnormal growth of cells in brain leads to the formation of tumors, which are categorized into benign and malignant. In this article, Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification based brain tumor detection and its grading system is proposed. It has two phases as brain tumor segmentation and brain tissue segmentation. In brain tumor segmentation, CANFIS classifier is used to classify the test brain image into benign or malignant. Then, morphological operations are applied over the malignant image in order to segment the tumor regions in brain image. The K‐means classifier is used to classify the brain tissues into Grey Matter (GM), White Matter (WM) and Cerebro Spinal Fluid (CSF) regions as three different classes. Next, the segmented tumor is graded as mild, moderate or severe based on the presence of segmented tumor region in brain tissues. 相似文献
Automatic cervical cancer segmentation in multimodal magnetic resonance imaging (MRI) is essential because tumor location and delineation can support patients' diagnosis and treatment planning. To meet this clinical demand, we present an encoder–decoder deep learning architecture which employs an EfficientNet encoder in the UNet++ architecture (E-UNet++). EfficientNet helps in effectively encoding multiscale image features. The nested decoders with skip connections aggregate multiscale features from low-level to high-level, which helps in detecting fine-grained details. A cohort of 228 cervical cancer patients with multimodal MRI sequences, including T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, contrast enhancement T1-weighted imaging, and dynamic contrast-enhanced imaging (DCE), has been explored. Evaluations are performed by considering either single or multimodal MRI with standard segmentation quantitative metrics: dice similarity coefficient (DSC), intersection over union (IOU), and 95% Hausdorff distance (HD). Our results show that the E-UNet++ model can achieve DSC values of 0.681–0.786, IOU values of 0.558–0.678, and 95% HD values of 3.779–7.411 pixels in different single sequences. Meanwhile, it provides DSC values of 0.644 and 0.687 on three DCE subsequences and all MRI sequences together. Our designed model is superior to other comparative models, which shows the potential to be used as an artificial intelligence tool for cervical cancer segmentation in multimodal MRI. 相似文献
Precise diagnostics are of significant importance to the optimal treatment outcomes of patients bearing brain tumors. NIR‐II fluorescence imaging holds great promise for brain‐tumor diagnostics with deep penetration and high sensitivity. This requires the development of organic NIR‐II fluorescent agents with high quantum yield (QY), which is difficult to achieve. Herein, the design and synthesis of a new NIR‐II fluorescent molecule with aggregation‐induced‐emission (AIE) characteristics is reported for orthotopic brain‐tumor imaging. Encapsulation of the molecule in a polymer matrix yields AIE dots showing a very high QY of 6.2% with a large absorptivity of 10.2 L g?1 cm?1 at 740 nm and an emission maximum near 1000 nm. Further decoration of the AIE dots with c‐RGD yields targeted AIE dots, which afford specific and selective tumor uptake, with a high signal/background ratio of 4.4 and resolution up to 38 µm. The large NIR absorptivity of the AIE dots facilitates NIR‐I photoacoustic imaging with intrinsically deeper penetration than NIR‐II fluorescence imaging and, more importantly, precise tumor‐depth detection through intact scalp and skull. This research demonstrates the promise of NIR‐II AIE molecules and their dots in dual NIR‐II fluorescence and NIR‐I photoacoustic imaging for precise brain cancer diagnostics. 相似文献
Light‐emitting semiconductor quantum dots (QDs) combined with magnetic resonance imaging contrast agents within a single nanoparticle platform are considered to perform as multimodal imaging probes in biomedical research and related clinical applications. The principles of their rational design are outlined and contemporary synthetic strategies are reviewed (heterocrystalline growth; co‐encapsulation or assembly of preformed QDs and magnetic nanoparticles; conjugation of magnetic chelates onto QDs; and doping of QDs with transition metal ions), identifying the strengths and weaknesses of different approaches. Some of the opportunities and benefits that arise through in vivo imaging using these dual‐mode probes are highlighted where tumor location and delineation is demonstrated in both MRI and fluorescence modality. Work on the toxicological assessments of QD/magnetic nanoparticles is also reviewed, along with progress in reducing their toxicological side effects for eventual clinical use. The review concludes with an outlook for future biomedical imaging and the identification of key challenges in reaching clinical applications. 相似文献
Necrosis is a form of cell death that occurs only under pathological conditions such as ischemic diseases and traumatic brain injury (TBI). Non-invasive imaging of the affected tissue is a key component of novel therapeutic interventions and measurement of treatment responses in patients. Here, we report a bimodal approach for the detection and monitoring of TBI. PEGylated poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs), encapsulating both near infrared (NIR) fluorophores and perfluorocarbons (PFCs), were targeted to necrotic cells. We used cyanine dyes such as IRDye 800CW, for which we have previously demonstrated specific targeting to intracellular proteins of cells that have lost membrane integrity. Here, we show specific in vivo detection of necrosis by optical imaging and fluorine magnetic resonance imaging (19F MRI) using newly designed PLGA NP(NIR700 + PFC)-PEG-800CW. Quantitative ex vivo optical imaging and 19F MR spectroscopy of NIR-PFC content in injured brain regions and in major organs were well correlated. Both modalities allowed the in vivo identification of necrotic brain lesions in a mouse model of TBI, with optical imaging being more sensitive than 19F MRI. Our results confirm increased blood pool residence time of PLGA NPs coated with a PEG layer and the successful targeting of TBI-damaged tissue. A single PLGA NP containing NIR-PFC enables both rapid qualitative optical monitoring of the TBI state and quantitative 3D information from deeper tissues on the extent of the lesion by MRI. These necrosis-targeting PLGA NPs can potentially be used for clinical diagnosis of brain injuries.
Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell partition. In this paper, we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain tumor. We considered watershed segmentation technique for brain tumor detection. The proposed methodology is divided as follows: pre-processing, computing foreground applying watershed, extract and supply features to machine learning algorithms. Consequently, this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain tumor. 相似文献
Metamaterials provide a powerful platform to probe and enhance nonlinear responses in physical systems toward myriad applications. Herein, the development of a coupled nonlinear metamaterial (NLMM) featuring a self‐adaptive response that selectively amplifies the magnetic field is reported. The resonance of the NLMM is suppressed in response to higher degrees of radio‐frequency excitation strength and recovers during a subsequent low excitation strength phase, thereby exhibiting an intelligent, or nonlinear, behavior by passively sensing excitation signal strength and responding accordingly. The nonlinear response of the NLMM enables us to boost the signal‐to‐noise ratio during magnetic resonance imaging to an unprecedented degree. These results provide insights into a new paradigm to construct NLMMs consisting of coupled resonators and pave the way toward the utilization of NLMMs to address a host of practical technological applications. 相似文献
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