Related papers: Multimodal Volume-Aware Detection and Segmentation…

Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes. Manual segmentation defines the current gold standard in…

Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain.…

We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks. Our approach is based on active learning and meta-learning. One of the difficulties in…

Brain metastases affect approximately between 20% and 40% of cancer patients and are commonly treated with radiotherapy or radiosurgery. Early prediction of recurrence following treatment could enable timely clinical intervention and…

Multi-spectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) that offers…

The purpose was to assess the clinical value of a novel DropOut model for detecting and segmenting brain metastases, in which a neural network is trained on four distinct MRI sequences using an input dropout layer, thus simulating the…

Brain lesion volume measured on T2 weighted MRI images is a clinically important disease marker in multiple sclerosis (MS). Manual delineation of MS lesions is a time-consuming and highly operator-dependent task, which is influenced by…

We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation. We create our ensemble by training each network on our dataset with 50% of our annotated lesions censored out. We also…

Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%. A quick and accurate diagnosis is crucial to increase the chance of survival. However, in medical analysis, the manual annotation and segmentation of a…

Despite significant progress in pixel-level medical image analysis, existing medical image segmentation models rarely explore medical segmentation and diagnosis tasks jointly. However, it is crucial for patients that models can provide…

Brain tumors, particularly glioblastoma, continue to challenge medical diagnostics and treatments globally. This paper explores the application of deep learning to multi-modality magnetic resonance imaging (MRI) data for enhanced brain…

Multi-modal magnetic resonance imaging (MRI) is a crucial method for analyzing the human brain. It is usually used for diagnosing diseases and for making valuable decisions regarding the treatments - for instance, checking for gliomas in…

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully…

Brain tumor is deliberated as one of the severe health complications which lead to decrease in life expectancy of the individuals and is also considered as a prominent cause of mortality worldwide. Therefore, timely detection and prediction…

Background: Brain tumor segmentation has a significant impact on the diagnosis and treatment of brain tumors. Accurate brain tumor segmentation remains challenging due to their irregular shapes, vague boundaries, and high variability.…

Deep learning-based brain tumor segmentation (BTS) models for multi-modal MRI images have seen significant advancements in recent years. However, a common problem in practice is the unavailability of some modalities due to varying scanning…

Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive,…

This paper proposes a 3D attention-based U-Net architecture for multi-region segmentation of brain tumors using a single stacked multi-modal volume created by combining three non-native MRI volumes. The attention mechanism added to the…

Background: This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM), using clinical, dosimetric, and pretherapeutic MRI data. Methods: In…

Recently deep learning has been playing a major role in the field of computer vision. One of its applications is the reduction of human judgment in the diagnosis of diseases. Especially, brain tumor diagnosis requires high accuracy, where…