This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 2.

BACKGROUND: Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role that image processing and machine learning techniques play in this evolution.
METHODS: We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users.
RESULTS: Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted-normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not.
CONCLUSIONS: This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment.

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.

UNASSIGNED: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer\'s disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.
UNASSIGNED: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.
UNASSIGNED: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.
UNASSIGNED: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Auditory impairment stands as a pervasive global issue, exerting significant effects on individuals\' daily functioning and interpersonal engagements. Cochlear implants (CIs) have risen as a cutting-edge solution for severe to profound hearing loss, directly stimulating the auditory nerve with electrical signals. The success of CI procedures hinges on precise pre-operative planning and post-operative evaluation, highlighting the significance of advanced three-dimensional (3D) inner ear reconstruction software. Accurate pre-operative imaging is vital for identifying anatomical landmarks and assessing cochlear deformities. Tools like 3D Slicer, Amira and OTOPLAN provide detailed depictions of cochlear anatomy, aiding surgeons in simulating implantation scenarios and refining surgical approaches. Post-operative scans play a crucial role in detecting complications and ensuring CI longevity. Despite technological advancements, challenges such as standardization and optimization persist. This review explores the role of 3D inner ear reconstruction software in patient selection, surgical planning, and post-operative assessment, tracing its evolution and emphasizing features like image segmentation and virtual simulation. It addresses software limitations and proposes solutions, advocating for their integration into clinical practice. Ultimately, this review underscores the impact of 3D inner ear reconstruction software on cochlear implantation, connecting innovation with precision medicine.

Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of a relative lack of review. To understand the trends of transformers in RS, we undertook a quantitative analysis of the major research on transformers over the past two years by dividing the application of transformers into eight domains: land use/land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers achieve a higher accuracy in LULC classification and fusion, with more stable performance in segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters than convolutional neural networks (CNNs). Additionally, further research is also needed regarding inference speed to improve transformers\' performance. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.

Chronic liver disease is responsible for significant morbidity and mortality worldwide. Abdominal computed tomography (CT) and magnetic resonance imaging (MRI) can fully visualise the liver and adjacent structures in the upper abdomen providing a reproducible assessment of the liver and biliary system and can detect features of portal hypertension. Subjective interpretation of CT and MRI in the assessment of liver parenchyma for early and advanced stages of fibrosis (pre-cirrhosis), as well as severity of portal hypertension, is limited. Quantitative and reproducible measurements of hepatic and splenic volumes have been shown to correlate with fibrosis staging, clinical outcomes, and mortality. In this review, we will explore the role of volumetric measurements in relation to diagnosis, assessment of severity and prediction of outcomes in chronic liver disease patients. We conclude that volumetric analysis of the liver and spleen can provide important information in such patients, has the potential to stratify patients\' stage of hepatic fibrosis and disease severity, and can provide critical prognostic information. CRITICAL RELEVANCE STATEMENT: This review highlights the role of volumetric measurements of the liver and spleen using CT and MRI in relation to diagnosis, assessment of severity, and prediction of outcomes in chronic liver disease patients. KEY POINTS: Volumetry of the liver and spleen using CT and MRI correlates with hepatic fibrosis stages and cirrhosis. Volumetric measurements correlate with chronic liver disease outcomes. Fully automated methods for volumetry are required for implementation into routine clinical practice.

BACKGROUND: Automated segmentation of 3-dimensional pulp space on cone-beam computed tomography images presents a significant opportunity for enhancing diagnosis, treatment planning, and clinical education in endodontics. The aim of this systematic review was to investigate the performance of artificial intelligence-driven automated pulp space segmentation on cone-beam computed tomography images.
METHODS: A comprehensive electronic search was performed using PubMed, Web of Science, and Cochrane databases, up until February 2024. Two independent reviewers participated in the selection of studies, data extraction, and evaluation of the included studies. Any disagreements were resolved by a third reviewer. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the risk of bias.
RESULTS: Thirteen studies that met the eligibility criteria were included. Most studies demonstrated high accuracy in their respective segmentation methods, although there was some variation across different structures (pulp chamber, root canal) and tooth types (single-rooted, multirooted). Automated segmentation showed slightly superior performance for segmenting the pulp chamber compared to the root canal and single-rooted teeth compared to multi-rooted ones. Furthermore, the second mesiobuccal (MB2) canalsegmentation also demonstrated high performance. In terms of time efficiency, the minimum time required for segmentation was 13 seconds.
CONCLUSIONS: Artificial intelligence-driven models demonstrated outstanding performance in pulp space segmentation. Nevertheless, these findings warrant careful interpretation, and their generalizability is limited due to the potential risk and low evidence level arising from inadequately detailed methodologies and inconsistent assessment techniques. In addition, there is room for further improvement, specifically for root canal segmentation and testing of artificial intelligence performance in artifact-induced images.

BACKGROUND: Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable.
METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined.
RESULTS: Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42.
CONCLUSIONS: This meta-analysis demonstrates DL algorithms\' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.