DE-UNeXt: Dual encoder UNeXt for intracranial hemorrhage segmentation on a novel HBU CH dataset

DFMA-ICH: a deformable mixed-attention model for intracranial hemorrhage lesion segmentation based on deep supervision

Intracranial hemorrhage (ICH) is a type of stroke with a high mortality rate and failing to localize even minor ICH can put a patient's life at risk. However, its patterns are diverse in shapes and sizes and, sometimes, even hard to recognize its existence. Therefore, it is challenging to accurately detect and localize diverse ICH patterns. In this article, we propose a novel Perihematomal Edema Guided Scale Adaptive R-CNN (PESA R-CNN) for accurate segmentation of various size hemorrhages with the goal of minimizing missed hemorrhage regions. In our approach, we design a Center Surround Difference U-Net (CSD U-Net) to incorporate Perihematomal Edema (PHE) for more accurate Region of Interest (RoI) generation. We trained CSD U-Net to predict PHE and hemorrhage regions as targets in a weakly supervised manner and utilized its prediction results to generate RoI. By including more informative features of PHE around hemorrhage, this enhanced RoI generation allows a model to reduce the false-negative rate. Furthermore, these expanded RoIs are aligned with the Scale Adaptive RoI Align (SARA) module based on their size to prevent the loss of fine-scale information and small hemorrhage patterns. Each scale adaptively aligned RoI is processed with the corresponding separate segmentation network of Multi-Scale Segmentation Network (MSSN), which integrates the results from each scale's segmentation network. In experiments, our model shows significant improvement on dice coefficient (0.697) and Hausdorff distance (12.918), compared to all other segmentation models. It also minimizes the number of missing small hemorrhage regions and enhances overall segmentation performance on diverse ICH patterns.

Intracranial hemorrhages in head CT scans serve as a first line tool to help specialists diagnose different types. However, their types have diverse shapes in the same type but similar confusing shape, size and location between types. To solve this problem, this paper proposes an all attention U-Net. It uses channel attentions in the U-Net encoder side to enhance class specific feature extraction, and space and channel attentions in the U-Net decoder side for more accurate shape extraction and type classification. The simulation results show up to a 31.8% improvement compared to baseline, ResNet50 + U-Net, and better performance than in cases with limited attention.

Introduction : Stroke is a leading cause of morbidity and mortality worldwide, with hemorrhagic strokes accounting for 10–20% of all strokes. Patients presenting with intracerebral hemorrhage (ICH) often face higher rates of mortality and poorer prognosis than those with other stroke types. As ICH treatment relies on in‐hospital neuroimaging findings, one potential barrier in the effective management of ICH includes increased time to ICH detection and treatment, particularly due to delays in imaging interpretation in busy hospitals and emergency departments. Artificial Intelligence (AI) driven software has recently been developed and become commercially available for the detection of Intracranial Hemorrhage (ICH) and Chronic Cerebral Microbleeds (CMBs). Such adjunct tools may enhance patient care by decreasing time to treatment and diagnosis by helping to adjudicate diagnoses in difficult cases. This systematic review aims to describe the current literature surrounding all currently existing AI algorithms for ICH detection with either non‐contrast computed tomography (CT) scans or CMBs detection with magnetic resonance imaging (MRI). Methods : Following PRISMA guidelines, MEDLINE and EMBASE were searched for studies published through March 1st, 2021, and all studies investigating AI algorithms for hemorrhage detection in non‐contrast CT scans or CMBs detection on MRI scans were eligible for inclusion. Any studies focusing on AI for hemorrhage segmentation only, including studies that enrolled patients with hemorrhages only as their study group, were excluded. Extracted data included development methods, training, validation and testing datasets, and accuracy metrics for each algorithm, when available. Meta‐analysis was not conducted due to heterogeneity in reported accuracy metrics and highly variant algorithmic development. The completed protocol is available for review in the PROSPERO registry. Results : After the removal of duplicates, a total of 609 studies were identified and screened. After an initial screening and full text review, 40 studies were included in this review. Of these, 18 tested a 2‐Dimensional (2D) convolutional neural network (CNN) AI algorithm, 3 used a purley 3‐Dimension (3D) CNN, and 2 utilized a hybrid 2D‐3D CNN. Of note, one software was able to identify ICH in the setting of ischemic stroke using MRI scans. Included papers noted the following challenges when developing these AI algorithms: extensive time required to create suitable datasets, the volumetric nature of the imaging exams, fine tuning the system, and focusing on the reduction of false positives. Diagnostic accuracy data was available for 21 of these studies, which reported a mean accuracy of 94.37% and a mean AUC of 0.958. Conclusions : As reported in this study, many AI‐driven software tools have been developed over the last 5 years. These tools have high diagnostic accuracy on average and have the potential to contribute to the diagnosis of ICH or CMBs with expert‐level accuracy. With time to treatment often dependent on time to diagnosis, this AI software may increase both the speed and accuracy of adjudicating diagnoses. Although there have been several obstacles faced by the developers of these algorithms, AI‐driven software is an important frontier for the future of clinical medicine.

Intracerebral hemorrhage (ICH) often leads to high disability and mortality rates, making accurate segmentation of hematoma regions critical for clinical assessment and treatment planning. Although deep learning has advanced ICH segmentation techniques, it still faces challenges due to the complexity of hematoma morphology and location. ICH-related a priori knowledge provides additional information about hematoma morphology, location, and evolution, which helps the model more accurately identify and segment hematoma regions. In this study, we propose the Depth-wise Contextual Attention with 3D Connectivity Constraints Network (DCC-Net), an architecture that enhances intracerebral hemorrhage segmentation accuracy through a synergistic dual-component design. The Depth-wise Contextual Attention (DCA) module leverages hematoma anatomical spatial continuity by employing a depth-wise local window attention mechanism to adaptively aggregate adjacent slice information, thereby strengthening intracranial hematoma feature representation. Additionally, the 3D Connectivity Constraint (3D-CC) loss ensures three-dimensional structural integrity through differentiable topological computations. This synergistic design collectively enhances model robustness for complex hematoma morphologies. We evaluated the proposed DCC-Net and compared it with current state-of-the-art methods. Tested using rigorous intracerebral hemorrhage evaluation metrics, the experimental results show that our method improves over the underlying model on most evaluation metrics and outperforms other existing techniques in key performance indicators.