Prostate cancer of magnetic resonance imaging automatic segmentation and detection of based on 3D-Mask RCNN

Background: Prostate cancer is a widespread form of cancer that impacts men across the world. MRI plays a pivotal role in the detection and precise localization of cancerous regions, aiding medical professionals in devising effective treatment strategies for patients. As a result, MRI is often used in the diagnosis of prostate cancer.Purpose: Our proposed method employs deep learning to achieve automatic segmentation and detection of prostate cancer in MRI single series, particularly T2-weighted imaging (T2WI).Materials and methods: Our study utilized data from 133 patients at a hospital, consisting of 71 cases of prostate cancer and 62 cases of benign prostatic tumors. We employed T2-weighted imaging (T2WI) MRI single series from 93 prostates as the training set for our 3D-Mask RCNN model, while the remaining data from 40 prostates were used for validation. The masks were manually delineated by an experienced radiologist, with pathology serving as the reference standard. Our approach was evaluated using several metrics, such as dice similarity coefficient (DSC), accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curve analysis.Results: Our study produced promising results using the 3D Mask R-CNN model. The training set yielded a DSC score of 0.856, sensitivity of 0.921, and specificity of 0.961. The test set was also successful, with a DSC score of 0.849, sensitivity of 0.911, and specificity of 0.931. Furthermore, our model achieved an AUC value of 0.865 and an accuracy, sensitivity, and specificity of 0.866, 0.875, and 0.835, respectively, for the training set. The test set had an AUC value of 0.842 and an accuracy, sensitivity, and specificity of 0.836, 0.847, and 0.819, respectively. These findings demonstrate that our approach is capable of accurately detecting and segmenting prostate cancer in MRI single series -T2WI.Conclusion: The use of the 3D-Mask RCNN model in segmenting prostate tumors and detecting cancer in MRI T2WI has been shown to be highly effective and precise. This approach has the potential to greatly benefit ra-diologists by improving the accuracy and efficiency of diagnoses, leading to more effective treatment planning for patients with prostate cancer. By automating the segmentation process, this approach can also reduce the workload of radiologists and increase the consistency of diagnoses. The high performance of this model high-lights the potential of deep learning techniques in medical imaging and demonstrates the significant impact that these approaches can have on improving patient outcomes.