###### Abstract  
Intracranial hemorrhage (ICH) is a hemorrhagic disease occurring in the ventricle or brain, but we found that the U-Net network has poor segmentation performance for small lesion areas. In order to improve the segmentation accuracy, a new convolutional neural network called MSRL-Net is proposed in this paper to accurately segment the lesion regions in the CT images of intracranial hemorrhage. Specifically, to avoid the problem of missing information in the downsampling process, we propose a strategy combining MaxPool and SoftPool. In addition, the mixed loss function is used to optimize the unbalance of medical images. Finally, at the bottleneck layer, an MRHDC module is designed to represent the rich spatial information in the underlying features, in order to obtain multi-scale features with different receptive fields. Our model achieves 0.712 average Dice on a dataset. The experimental results show that this model has a good segmentation effect and potential clinical prospects. intracerebral hemorrhage; U-Net model; MSpool; coordinate attention +
Footnote †: journal: Medical Image Analysis  
**Citation:** [PERSON]; [PERSON]. MSRL-Net: An Automatic Segmentation of Intracranial Hemorrhage for CT Images Based on the U-Net Framework. **Algorithm** (ICH) is a simple algorithm for the classification of the lesions of intracranial hemorrhage. However, this process is labor-intensive and the accuracy of the analysis depends on the clinician's expertise. Discrepancies often arise between different doctors' conclusions. In remote regions, where medical resources are scarce, few doctors possess the skills to interpret CT images, and specialized radiologists may be absent. Misdiagnoses can severely impact patients' subsequent treatments. The situation will improve to some extent in hospitals with relatively rich resources; however, due to the number of doctors or the current workload, the misdiagnosis rate is still high [7]. To correct this, it is necessary to develop an algorithm that is capable of automatically segmenting the focal area. As artificial intelligence continues to advance, computer-aided diagnosis has garnered significant attention, becoming a focal point in current research. Computer-aided algorithms enable automated analysis of medical influences, generating detection reports for physicians to review CT scans, thereby enhancing their work efficiency, reducing pressure, and decreasing misdiagnosis rates. Consequently, developing an accurate ICH diagnostic support system is essential. In the prevalent deep learning environment, numerous researchers have investigated automatic ICH segmentation, leading to the creation of various medical image segmentation networks, such as U-Net and U-Net++ [8]. However, these classical networks still face challenges in clinical trials. They primarily focus on large lesion areas, struggling to identify small lesion areas. For instance, CT images' complexity and cerebral hemorrhage features, resembling the skull, pose considerable difficulties for automatic segmentation. Moreover, the ICH lesion area's small, intricate shapes and unfixed locations reduce segmentation accuracy. To address these issues, this study develops a pure convolution-based U-Net network for automatic ICH segmentation. Because the U-Net network has fewer parameters and combines low-resolution information (to provide object category recognition basis) and high-resolution information (to provide accurate segmentation and positioning basis), it is perfectly suitable for medical image segmentation. The specific approach is as follows:  
1. Preprocessing CT images to remove skull features using OpenCV, thereby highlighting lesion areas without interference from other factors. 2. Designing an MSpool module to replace traditional max-pooling, reducing information loss during network downsampling and enhancing segmentation accuracy. 3. Adopting a multi-objective joint loss function to address pixel classification imbalance. 4.