Brain Tumor Imaging Research Articles

Application of convolutional neural networks in image classification and applications of improved convolutional neural networks

Abstract. This paper reviews the application and improvement of convolutional neural networks (CNNs) in image classification. Firstly, a shallow CNN for interstitial lung disease image classification is presented. This model suppresses overfitting through a unique network architecture and optimisation algorithm. Next, the improved VGG16 architecture and MIDNet18 model are discussed and their superior performance in brain tumour image classification is demonstrated. Subsequently, a CNN-CapsNet model for cervical cancer image classification and its improvement are presented and the customised model is compared with the conventional VGG-16 CNN architecture in the paper. Next, the application of sparse convolutional kernels and hybrid sparse convolutional kernels (HDCs) in solving the problem of computational resource consumption is presented. Subsequently, methods for solving the problem of limited training data through transfer learning and network data augmentation techniques are discussed, as well as GAN-generated datasets for solving the overfitting problem. Finally, the effect of degraded images on the classification effectiveness of CNNs is explored. The results show that the improved CNN architecture and algorithms have significant effects in solving the problems of overfitting and computational resource consumption, and can significantly improve the accuracy and efficiency of image classification. And degraded images do adversely affect the accuracy of CNN for image classification.

Synthesis, preclinical assessment, and first-in-human study of [18F]d4-FET for brain tumor imaging.

Tumor-to-background ratio (TBR) is a critical metric in oncologic PET imaging. This study aims to enhance the TBR of [18F]FET in brain tumor imaging by substituting deuterium ("D") for hydrogen ("H"), thereby improving the diagnostic sensitivity and accuracy. [18F]d4-FET was synthesised by two automated radiochemistry modules. Biodistribution studies and imaging efficacy were evaluated in vivo and ex vivo in rodent models, while metabolic stability and radiation dosimetry were assessed in non-human primates. Additionally, preliminary imaging evaluations were carried out in five brain tumor patients: three glioma patients underwent imaging with both [18F]d4-FET and [18F]FET, and two patients with brain metastases were imaged using [18F]d4-FET and [18F]FDG. [18F]d4-FET demonstrated high radiochemical purity and yield. PET/MRIin rodent models demonstrated superior TBR for [18F]d4-FET compared to [18F]FET, and autoradiography showed tumor margins that correlated well with pathological extents. Studies in cynomolgus monkeys indicated comparable in vivo stability and effective dose with [18F]FET. In glioma patients, [18F]d4-FET showed enhanced TBR, while in patients with brain metastases, [18F]d4-FET displayed superior lesion delineation compared to [18F]FDG, especially in smaller metastatic sites. We successfully synthesized the novel PET radiotracer [18F]d4-FET, which retains the advantageous properties of [18F]FET while potentially enhancing TBR for glioma imaging. Preliminary studies indicate excellent stability, efficacy, and sensitivity of [18F]d4-FET, suggesting its potential in clinical evaluations of brain tumors. ChiCTR2400081576, registration date: 2024-03-05, https://www.chictr.org.cn/bin/project/edit?pid=206162.

A New Acquisition Protocol for Optimized Dynamic Susceptibility Perfusion Imaging of Brain Tumors.

A New Acquisition Protocol for Optimized Dynamic Susceptibility Perfusion Imaging of Brain Tumors.

MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism

AbstractAccurate segmentation of brain tumor magnetic resonance imaging (MRI) is crucial for treatment planning. Addressing the challenges of complex tumor structures and inadequate cross‐channel information utilization in Unet‐based segmentation, this paper proposes the multi‐scale residual brain tumor MRI segmentation network (MRS‐Net) incorporating an attention mechanism to enhance segmentation accuracy. First, the double residual feature fusion module is utilized to enhance the fusion of feature information between different levels. Second, the Atrous Spatial Pyramid Pooling is introduced as a bridging module of the network to capture the features at different scales of the image, so as to enhance the extraction capability of the network for detailed features. Finally, the inverted residual coordinate attention module replaces the direct splicing in Unet to fuse the large feature information at each level and scale, thus enhancing the model's ability to recognize the spatial location information of brain tumors. The Dice coefficients, positive predictive values (PPVs), sensitivities (Sensitivity) and Hausdorff distance (HD), which are the four evaluation indexes, reach 84.54%, 87.43%, 88.37% and 2.248, respectively, which are improved by 1.85%, 2.11%, 2.88% and 6.0%, respectively, compared with Unet. The experimental results show that MRS‐Net achieves better brain tumor image segmentation.

Efficient and Compressed Deep Learning Model for Brain Tumour Classification With Explainable AI for Smart Healthcare and Information Communication Systems

ABSTRACTThe detection of brain tumours presents a significant challenge in the medical domain, where prompt and precise diagnosis is crucial as patient outcomes depend on it. Conventional deep neural networks perform well in carrying out various imaging tasks within the healthcare sector; however, their effectiveness often falls short of expectations in practical applications due to the substantial computational resources required and issues with reliability. In this research, an optimised and effective deep learning model founded on the DenseNet‐169 architecture is introduced for the classification of magnetic resonance imaging brain tumours, which is particularly advantageous for smart healthcare systems and information and communication technology (ICT) settings with limited computational capabilities. The model compression methodologies, including pruning and quantization, have been employed to significantly diminish the dimensions and intricacy of the model while achieving a classification accuracy of 97.07%. Furthermore, this endeavour necessitates the enhancement of the model's interpretability through the utilisation of explainable artificial intelligence methodologies such as Gradient‐weighted Class Activation Mapping (Grad‐CAM) and SHapley Additive exPlanations (SHAP), which will aid clinicians in highlighting crucial areas of the images and validating feature importance concerning the decisions rendered by the model. A comparative performance evaluation is conducted against DenseNet‐169, ResNet‐50 and various other models to delineate the superior efficacy of our model, rendering it exceptionally adept for knowledge‐driven, real‐time brain tumour diagnosis within smart healthcare and ICT systems where resources are constrained.

A New Tool for Extracting Static and Dynamic Parameters from [18F]F-DOPA PET/CT in Pediatric Gliomas

Background/Objectives: PET imaging with [18F]F-DOPA has demonstrated high potential for the evaluation and management of pediatric brain gliomas. Manual extraction of PET parameters is time-consuming, lacks reproducibility, and varies with operator experience. Methods: In this study, we tested whether a semi-automated image processing framework could overcome these limitations. Pediatric patients with available static and/or dynamic [18F]F-DOPA PET studies were evaluated retrospectively. We developed a Python software to automate clinical index calculations, including preprocessing to delineate tumor volumes from structural MRI, accounting for lesions with low [18F]F-DOPA uptake. A total of 73 subjects with treatment-naïve low- and high-grade gliomas, who underwent brain MRI within two weeks of [18F]F-DOPA PET, were included and analyzed. Static analysis was conducted on all subjects, while dynamic analysis was performed on 32 patients. Results: For 68 subjects, the Intraclass Correlation Coefficient for T/S between manual and ground truth segmentation was 0.91. Using our tool, ICC improved to 0.94. Our method demonstrated good reproducibility in extracting static tumor-to-striatum ratio (p = 0.357); however, significant differences were observed in tumor slope (p < 0.05). No significant differences were found in time-to-peak (p = 0.167) and striatum slope (p = 0.36). Conclusions: Our framework aids in analyzing [18F]F-DOPA PET images of pediatric brain tumors by automating clinical score extraction, simplifying segmentation and Time Activity Curve extraction, reducing user variability, and enhancing reproducibility.

A promise of nose to brain delivery of bevacizumab intranasal sol-gel formulation substantiated in rat C6 glioma model.

Glioblastoma is one of the rapidly spreading cancers, with its potent malignancy often linked to pronounced angiogenesis within tumors. To mitigate this vascularization profile, bevacizumab (Avastin®), a monoclonal antibody, has been utilized for its antiangiogenic activity. However, its effectiveness is hindered by challenges in crossing the blood-brain barrier and the risk of off-target organ toxicity. Delivering drugs directly from the nose to the brain through the olfactory or trigeminal nerves bypassing the blood-brain barrier offers enhanced bioavailability and a more precise targeting strategy. To overcome these challenges, we aimed to develop bevacizumab in situ gel loaded mesoporous silica nanoparticles for intranasal delivery and further examine their pharmacokinetic and pharmacodynamic characteristics. The intranasal gel of mesoporous silica nanoparticles loaded with bevacizumab was optimized and formulated using a factorial and quality by design approach. In the case of bevacizumab mesoporous silica nanoparticles, lower particle size and most negative zeta potential were selected as quality target product profiles which is important for drug loading on the mesoporous silica nanoparticles and also transport of these nanoparticles across the nasal mucosa to the brain. A design space with a multidimensional combination of input variables and process parameters has been demonstrated to assure quality. To optimize the design space and achieve the desired quality standards, the base catalyst and surfactant concentration were chosen as the critical process parameters, while particle size and zeta potential were identified as the critical quality attributes. The novel formulation was assessed for physicochemical parameters such as particle size, zeta potential, entrapment efficiency, appearance, color, consistency, and pH. Additionally, studies on in vitro release, ex vivo permeation, stability, nasal toxicity, organ safety, and bioavailability were conducted. The efficacy study was conducted in an orthotopic murine glioblastoma rat model in which C6 Luc cells were instilled in the striatum of the rat's brain. In vivo, bioluminescence imaging of brain tumors was carried out to observe the tumor regression after treatment with the intranasal and intravenous bevacizumab formulation. Biochemical parameters and histopathology were performed for organ safety studies. The optimized intranasal formulation exhibited an average particle size of 318.8nm and a zeta potential of - 14.7mV for the mesoporous silica nanoparticles. The formulation also demonstrated an entrapment efficiency of 91.34% and a loading capacity of 45.67%. Further pharmacokinetic studies revealed that the optimized intranasal bevacizumab formulation achieved a significantly higher brain concentration Cmax = 147.9ng/ml, indicating improved bioavailability compared to rats administered with intravenous bevacizumab formulation (BEVATAS®), which had a Cmax of 127.2ng/ml. Moreover, this nanoparticle formulation entirely mitigated systemic exposure to bevacizumab. Organ safety evaluation of different biochemical parameters and histopathological analyses revealed that the intranasal bevacizumab-treated group was showing less off-target organ toxicity compared to the group treated with intravenous bevacizumab formulation. Subsequently, the efficacy of this nanosystem was evaluated in an orthotopic glioblastoma rat model, monitoring tumor growth over time through in vivo bioluminescence imaging and assessing anti-angiogenic effects. Twenty-one days post-induction, mesoporous silica nanoparticles loaded with bevacizumab in situ gel exhibited a marked reduction in average bioluminescence radiance (4.39 × 103) from day 7 (1.35 × 107) emphasizing an enhanced anti-angiogenic effect compared to the group treated with intravenous bevacizumab formulation which showed a gradual decrease in average bioluminescence radiance (4.82 × 104) from day 7 (1.42 × 107). These results suggest that the proposed novel formulation of mesoporous silica nanoparticles loaded bevacizumab in situ gel could serve as a promising avenue to enhance glioblastoma treatment efficacy, thereby potentially improving patient quality of life and survival rates significantly. Furthermore, the success of this delivery method could open new avenues for treating other neurological disorders, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and stroke. By providing effective brain-targeted therapies, this approach has the potential to revolutionize treatment options and improve outcomes for a broad spectrum of neurological conditions.

Finely tunable NIR‐II organic scaffolds for fluorescence/photoacoustic duplex imaging–guided noninvasive photothermal therapy of glioblastoma

AbstractOrganic agents possessing NIR‐II and photoacoustic duplex imaging capabilities, coupled with high‐efficiency photothermal conversion, offer significant potential for noninvasive and precise phototheranostics of glioblastoma, which is further augmented when these agents can concurrently exhibit tumor targeting and blood–brain barrier (BBB) permeability. This study reports a series of finely tunable NIR‐II molecular luminophores based on the aza‐BODIPY scaffold, featuring unique twisted and rotatable structures. They are further constructed to folate‐decorated polymeric nanoparticles, exhibiting remarkable NIR‐II/photoacoustic imaging performance and superior photothermal conversion efficiency (49.7%). Folate modification enables tumor targeting and BBB permeability through receptor‐mediated transcytosis, allowing for precise and efficient phototherapy in 4T1‐/glioblastoma‐bearing mice after a single intravenous injection and irradiation. This study presents a rational molecular engineering approach and a versatile structural scaffold for designing NIR‐II emitters with tailored photophysical properties and desirable phototherapeutic efficacy, thereby offering novel perspectives on the development of advanced depth imaging probes and brain tumor therapeutics.

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

Robust brain MRI image classification with SIBOW-SVM

Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) is the most effective technique for brain tumor detection, generating comprehensive brain scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning methods, such as Convolutional Neural Networks (CNNs), have demonstrated significantly higher accuracy than manual screening. However, deep learning-based image classification methods, including CNNs, face challenges in estimating class probabilities without proper model calibration (Guo et al., 2017; Minderer et al., 2021). In this paper, we propose a novel brain tumor image classification method called SIBOW-SVM, which integrates the Bag-of-Features model with SIFT feature extraction and weighted Support Vector Machines. This new approach can effectively extract hidden image features, enabling differentiation of various tumor types, provide accurate label predictions, and estimate probabilities of images belonging to each class, offering high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive image datasets. To benchmark our method, we apply SIBOW-SVM to a public dataset of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results demonstrate that the new method outperforms state-of-the-art techniques, including CNNs, in terms of uncertianty quantification, classification accuracy, computational efficiency, and data robustness.

Lightweight MRI Brain Tumor Segmentation Enhanced by Hierarchical Feature Fusion.

Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries. For this issue, a lightweight MRI brain tumor segmentation method, enhanced by hierarchical feature fusion (EHFF), is proposed. This method reduces model parameters while improving segmentation performance by integrating hierarchical features. Initially, a fine-grained feature adjustment network is crafted and guided by global contextual information, leading to the establishment of an adaptive feature learning (AFL) module. This module captures the global features of MRI brain tumor images through macro perception and micro focus, adjusting spatial granularity to enhance feature details and reduce computational complexity. Subsequently, a hierarchical feature weighting (HFW) module is constructed. This module extracts multi-scale refined features through multi-level weighting, enhancing the detailed features of spatial positions and alleviating the lack of attention to local position details in macro perception. Finally, a hierarchical feature retention (HFR) module is designed as a supplementary decoder. This module retains, up-samples, and fuses feature maps from each layer, thereby achieving better detail preservation and reconstruction. Experimental results on the BraTS 2021 dataset demonstrate that the proposed method surpasses existing methods. Dice similarity coefficients (DSC) for the three semantic categories ET, TC, and WT are 88.57%, 91.53%, and 93.09%, respectively.

CAPSULE NETWORK FOR BRAIN TUMORS SEGMENTATION IN MRI IMAGES

Glioma is an abnormal, irregularly shaped cell growth in the brain. Diagnosing glioma often relies on magnetic resonance imaging (MRI), which requires precise segmentation of the tumor for effective analysis. Convolutional Neural Networks (CNN), such as U-Net, is widely used in medical image processing, particularly for image segmentation tasks. However, CNNs have the limitation in effectively learning spatial relationships within images. To address this, Capsule Networks (CapsNet) is introduced, utilizing capsule dynamic routing to better capture spatial hierarchies. This paper aims to investigate the performance of SegCaps, a segmentation model based on Capsule Networks, for brain glioma segmentation in MRI images, compared to the CNN-based U-Net model. Both models were tested on the BraTS2018 glioma dataset, which includes 374 MRI images of brain tumors across four modalities (T1, T1c, T2, FLAIR). The performance of SegCaps and U-Net was evaluated using two key segmentation metrics: Dice coefficient and Jaccard index. The results show that SegCaps outperformed U-Net with a Dice coefficient of 87.96% compared to U-Nets 85.56%, demonstrating a 2.4% improvement. Additionally, SegCaps required fewer parameters than the U-Net model, highlighting its efficiency. In conclusion, SegCaps can be considered as a promising alternative for glioma segmentation in MRI images. Future work could focus on refining the SegCaps model to enhance its performance while reducing computational costs.

Improved Brain Tumor Detection MRI Using Advanced Processing Techniques: Enhancement and Convolution Case Studies

Brain tumors present a significant challenge in medical imaging due to their complexity, requiring early detection and precise analysis for effective treatment. This study develops and evaluates advanced image processing workflows aimed at enhancing brain tumor image analysis. The proposed method involves four main steps: enlargement, pre-processing with min-max filters, enhancement, and convolution. The dataset used is from Kaggle, comprising 3,364 images categorized into Glioma (100 images), Meningioma (115 images), No Tumor (105 images), and Pituitary Tumor (74 images). For this study, images from the Glioma, Meningioma, and Pituitary Tumor categories were used, with one image selected from each category for technique evaluation. The results showed significant improvements in image clarity and detail, with high correlation values of 0.9851 for Meningioma and 0.9886 for Pituitary. These findings highlight the effectiveness of the proposed techniques in enhancing image quality and diagnostic accuracy.

Dual phase hierarchical brain tumour detection and segmentation using unet based skip guidance residual convolutional transformer

Brain tumour segmentation and classification is an important approach to understanding tumour characteristics effectively. Accurate lesion segmentation requires many image modalities with varied contrasts. As a result, manual segmentation may not be suitable for large-scale research. The research has shown interest in developing various learning algorithms to detect and segment brain tumour (BT) images accurately based on the set of experimental images. Some of the issues noticed in the existing models, such as high computational complexity, high processing time, and less accuracy, were identified. In order to overcome these existing issues, a novel Dual-phase channel attentional Residual convolutional transformer Unet model (DCARCTUnet) is introduced for segmenting brain tumours. Initially, the input images are passed into the pre-processing stage to remove noise and blur. The Efficient Net model identified the pre-processed image as either normal or tumour. The proposed segmentation model can be integrated into three stages, namely dual phase channel attention module (DCAM), residual convolutional transformer Unet model and Pyramid skip guidance module (PSM). The proposed method uses the BRATS 2020–2021 dataset to analyze the results accurately. The results showed that the proposed model has a dice coefficient value of 0.99 and a Jaccard coefficient value of 0.98. Furthermore, performance analysis is carried out to demonstrate the effectiveness of the proposed model.

Enhancing brain tumor classification through ensemble attention mechanism.

Brain tumors pose a serious threat to public health, impacting thousands of individuals directly or indirectly worldwide. Timely and accurate detection of these tumors is crucial for effective treatment and enhancing the quality of patients' lives. The widely used brain imaging technique is magnetic resonance imaging, the precise identification of brain tumors in MRI images is challenging due to the diverse anatomical structures. This paper introduces an innovative approach known as the ensemble attention mechanism to address this challenge. Initially, the approach uses two networks to extract intermediate- and final-level feature maps from MobileNetV3 and EfficientNetB7. This assists in gathering the relevant feature maps from the different models at different levels. Then, the technique incorporates a co-attention mechanism into the intermediate and final feature map levels on both networks and ensembles them. This directs attention to certain regions to extract global-level features at different levels. Ensemble of attentive feature maps enabling the precise detection of various feature patterns within brain tumor images at both model, local, and global levels. This leads to an improvement in the classification process. The proposed system was evaluated on the Figshare dataset and achieved an accuracy of 98.94%, and 98.48% for the BraTS 2019 dataset which is superior to other methods. Thus, it is robust and suitable for brain tumor detection in healthcare systems.

Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism

In this paper, an improved model of medical image segmentation for brain tumor is discussed, which is a deep learning algorithm based on U-Net architecture. Based on the traditional U-Net, we introduce GSConv module and ECA attention mechanism to improve the performance of the model in medical image segmentation tasks. With these improvements, the new U-Net model is able to extract and utilize multi-scale features more efficiently while flexibly focusing on important channels, resulting in significantly improved segmentation results. During the experiment, the improved U-Net model is trained and evaluated systematically. By looking at the loss curves of the training set and the test set, we find that the loss values of both rapidly decline to the lowest point after the eighth epoch, and then gradually converge and stabilize. This shows that our model has good learning ability and generalization ability. In addition, by monitoring the change in the mean intersection ratio (mIoU), we can see that after the 35th epoch, the mIoU gradually approaches 0.8 and remains stable, which further validates the model. Compared with the traditional U-Net, the improved version based on GSConv module and ECA attention mechanism shows obvious advantages in segmentation effect. Especially in the processing of brain tumor image edges, the improved model can provide more accurate segmentation results. This achievement not only improves the accuracy of medical image analysis, but also provides more reliable technical support for clinical diagnosis. To sum up, the improved U-Net model based on GSConv module and ECA attention mechanism proposed in this paper provides a new solution for brain tumor medical image segmentation, and its superior performance helps to improve disease detection and treatment effects, which is of great significance in related fields. In the future, we hope to further explore the application potential of this method in other types of medical image processing to advance the development of medical imaging.

Value of 11C-Methionine PET Imaging in High-Grade Gliomas: A Narrative Review.

11C-Methionine (MET) is a widely utilized amino acid tracer in positron emission tomography (PET) imaging of primary brain tumors. 11C-MET PET offers valuable insights for tumor classification, facilitates treatment planning, and aids in monitoring therapeutic response. Its tracer properties allow better delineation of the active tumor volume, even in regions that show no contrast enhancement on conventional magnetic resonance imaging (MRI). This review focuses on the role of MET-PET in brain glioma imaging. The introduction provides a brief clinical overview of the problems of high-grade and recurrent gliomas. It discusses glioma management, radiotherapy planning, and the difficulties of imaging after chemoradiotherapy (pseudoprogression or radionecrosis). The mechanism of MET-PET is described. Additionally, the review encompasses the application of MET-PET in the context of primary gliomas, addressing its diagnostic precision, utility in tumor classification, prognostic value, and role in guiding biopsy procedures and radiotherapy planning.

Evaluating the Impact of Filtering Techniques on Deep Learning-Based Brain Tumour Segmentation

Gliomas are a common and aggressive kind of brain tumour that is difficult to diagnose due to their infiltrative development, variable clinical presentation, and complex behaviour, making them an important focus in neuro-oncology. Segmentation of brain tumour images is critical for improving diagnosis, prognosis, and treatment options. Manually segmenting brain tumours is time-consuming and challenging. Automatic segmentation algorithms can significantly improve the accuracy and efficiency of tumour identification, thus improving treatment planning and outcomes. Deep learning-based segmentation tumours have shown significant advances in the last few years. This study evaluates the impact of four denoising filters, namely median, Gaussian, anisotropic diffusion, and bilateral, on tumour detection and segmentation. The U-Net architecture is applied for the segmentation of 3064 contrast-enhanced magnetic resonance images from 233 patients diagnosed with meningiomas, gliomas, and pituitary tumours. The results of this work demonstrate that bilateral filtering yields superior outcomes, proving to be a robust and computationally efficient approach in brain tumour segmentation. This method reduces the processing time by 12 epochs, which in turn contributes to lowering greenhouse gas emissions by optimizing computational resources and minimizing energy consumption.

Dual Channel‐Spatial Self‐Attention Transformer and CNN synergy network for 3D medical image segmentation

Even though the Vision Transformer leverages the self-attention mechanism to capture long-range dependencies, showing significant potential in medical image segmentation, the limited annotations in the image dataset make it difficult for the Transformer model to extract different global features, resulting in attention collapse and generating similar or identical attention maps. Previous studies have attempted to solve the problem by integrating convolutional neural layers into Transformer-based architectures. However, improper integration may lead to the inability of the model to effectively capture local and global information in both spatial and channel dimensions. To address the above issue, we propose a hybrid architecture using the Dual Channel-Spatial Self-Attention Transformer and CNN Synergy Network (DTC-SUNETR) for medical image segmentation. Specifically, we redesigned the self-attention mechanism. A novel Channel-Spatial Self-Attention (CSSA) block is introduced to integrate the enhanced channel and spatial self-attention mechanism to capture the global relationship and local structure among image features. This helps the model to more comprehensively understand the inter-dependencies between different channels and capture the relationships between different pixels, thus enhancing the feature representation of the corresponding dimensions. Simultaneously, it also improves the overall computational efficiency of the network. Extensive experiments on four different medical image segmentation datasets, including Synapse, ACDC, Brain Tumor, and Lung Tumor, demonstrate the superiority of the proposed DTC-SUNETR over state-of-the-art methods.

Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis

Intravital 2P-microscopy enables the longitudinal study of brain tumor biology in superficial mouse cortex layers. Intravital microscopy of the white matter, an important route of glioblastoma invasion and recurrence, has not been feasible, due to low signal-to-noise ratios and insufficient spatiotemporal resolution. Here, we present an intravital microscopy and artificial intelligence-based analysis workflow (Deep3P) that enables longitudinal deep imaging of glioblastoma up to a depth of 1.2 mm. We find that perivascular invasion is the preferred invasion route into the corpus callosum and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we observe morphological changes after white matter infiltration, a potential basis of an imaging biomarker during early glioblastoma colonization. Taken together, Deep3P allows for a non-invasive intravital investigation of brain tumor biology and its tumor microenvironment at subcortical depths explored, opening up opportunities for studying the neuroscience of brain tumors and other model systems.