ROXSI: Robust Cross-Sequence Semantic Interaction for Brain Tumor Segmentation on Multi-Sequence MR Images.

Deep learning-based brain tumor segmentation on multi-sequence magnetic resonance imaging (MRI) has gained widespread attention due to its great potential in supporting brain disease diagnosis. Although, compared to single-sequence images, more information is available from multi-sequence MR images, noise and artifacts on any given MR sequence can result in significant performance degradations. As in clinical routine, it is not always possible to maintain high imaging quality across all MR sequences (e.

Weakly supervised learning for multi-class medical image segmentation via feature decomposition.

Weakly supervised learning with image-level labels, releasing deep learning from highly labor-intensive pixel-wise annotation, has gained great attention for medical image segmentation. However, existing weakly supervised methods are mainly designed for single-class segmentation while leaving multi-class medical image segmentation rarely-explored. Different from natural images, label symbiosis, together with location adjacency, are much more common in medical images, making it more challenging for multi-class segmentation.

NEDD4-2 and the CLC-2 channel regulate neuronal excitability in the pathogenesis of mesial temporal lobe epilepsy.

An increasing number of studies have focused on the role of NEDD4-2 in regulating neuronal excitability and the mechanism of epilepsy. However, the exact mechanism has not yet been elucidated. Here, we explored the roles of NEDD4-2 and the CLC-2 channel in regulating neuronal excitability and mesial temporal lobe epilepsy (MTLE) pathogenesis.

HCN1 pathogenic variants associated with childhood epilepsy in a cohort of Chinese patients.

Objective: HCN ion channel family has a widespread expression in neurons, and recently, increasing studies have demonstrated their roles in epilepsies.

Methods: Clinical data of the patients were gathered in a retrospective study. Exon sequencing was used for the patients with unexplained recurrent seizures and varying levels of developmental delay.

Cluster-Re-Supervision: Bridging the Gap Between Image-Level and Pixel-Wise Labels for Weakly Supervised Medical Image Segmentation.

Weakly supervised learning, releasing deep learning from highly labor-intensive pixel-wise annotations, has gained great attention, especially for medical image segmentation. With only image-level labels, pixel-wise segmentation/localization usually is achieved based on class activation maps (CAMs) containing the most discriminative regions. One common consequence of CAM-based approaches is incomplete foreground segmentation, i.

Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage.

Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosis. Unfortunately, cerebral ventricle segmentation in complex clinical cases, such as in the coexistence with other lesions/hemorrhages, remains unexplored. In this paper, we, for the first time, focus on cerebral ventricle segmentation with the presence of intra-ventricular hemorrhages (IVH).

Fracture R-CNN: An anchor-efficient anti-interference framework for skull fracture detection in CT images.

Background: Skull fracture, as a common traumatic brain injury, can lead to multiple complications including bleeding, leaking of cerebrospinal fluid, infection, and seizures. Automatic skull fracture detection (SFD) is of great importance, especially in emergency medicine.

Purpose: Existing algorithms for SFD, developed based on hand-crafted features, suffer from low detection accuracy due to poor generalizability to unseen samples.

Uncertainty-Aware Deep Learning With Cross-Task Supervision for PHE Segmentation on CT Images.

Perihematomal edema (PHE) volume, surrounding spontaneous intracerebral hemorrhage (SICH), is an important biomarker for the presence of SICH-associated diseases. However, due to irregular shapes and extremely low contrast of PHE on CT images, manually annotating PHE in pixel-wise is time-consuming and labour intensive even for experienced experts, which makes it almost infeasible to deploy current supervised deep learning approaches for automated PHE segmentation. How to develop annotation-efficient deep learning to achieve accurate PHE segmentation is an open problem.

Ψ-Net: Focusing on the border areas of intracerebral hemorrhage on CT images.

Background And Objective: The volume of the intracerebral hemorrhage (ICH) obtained from CT scans is essential for quantification and treatment planning. However,a fast and accurate volume acquisition brings great challenges. On the one hand, it is both time consuming and operator dependent for manual segmentation, which is the gold standard for volume estimation.