Study on segmentation and prediction of intracerebral hemorrhage based on deep learning

With the aggravation of the aging population, the incidence of spontaneous intracerebral hemorrhage remains high. As a serious acute cerebrovascular disease, its timely and accurate diagnosis is crucial for the prognosis of patients. The segmentation of brain hemorrhage region image is an important step in medical image analysis, which is of great significance in assisting doctors to formulate treatment plans and evaluate the progress of the disease as well. Traditional manual segmentation methods are time-consuming and subjective which makes it difficult to meet the needs of clinical rapid diagnosis. With the development of deep learning technology, significant results in medical image segmentation have achieved. Therefore, it is of great significance to build a medical image automation segmentation model based on deep learning technology to annotate the hemorrhage region in the patient's head CT axial image accurately and efficiently. Based on this, the paper evaluates the effect of Jun Ma's U-Mamba network on the segmentation and prediction of brain hemorrhage regions. The experimental results show that this network model can achieve an accurate and efficient segmentation and prediction of the bleeding region in the brain CT image. By estimate, the Dice coefficient, IoU, average precision and average recall all above 0.9.