论文标题
一种新型的面膜R-CNN模型，以通过图像减法分段分段异质脑肿瘤
A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction
论文作者
论文摘要
疾病的细分是机器学习领域研究人员探索的一个流行主题。脑肿瘤非常危险，需要最高的精度才能进行成功手术。肿瘤患者通常接受4次MRI扫描，T1，T1GD，T2和FLAIR，然后将其发送给放射科医生进行细分并分析可能的未来手术。为了创建第二个细分，对放射科医生和患者的结论更加自信，这将是有益的。我们建议使用一个名为“图像分割”的放射科医生执行的方法，并将其应用于机器学习模型以证明更好的分割。使用蒙版R-CNN，其重新NET主链在RSNA肺炎检测挑战数据集上进行了预训练，我们可以在BRATS2020脑肿瘤数据集上训练模型。生物医学图像计算与分析中心提供了有关有或没有脑肿瘤患者以及相应分割的患者的MRI数据。我们可以通过将图像减法的方法与模型进行比较，而不会通过骰子系数（F1得分），回忆和未触及的测试集的精度进行比较，从而看到了图像减法的方法。我们的模型以0.75的骰子系数与没有图像减法的0.69相比进行。为了进一步强调图像减法的有用性，我们将最终模型与当前的最新模型进行比较与从MRI扫描分割肿瘤。
The segmentation of diseases is a popular topic explored by researchers in the field of machine learning. Brain tumors are extremely dangerous and require the utmost precision to segment for a successful surgery. Patients with tumors usually take 4 MRI scans, T1, T1gd, T2, and FLAIR, which are then sent to radiologists to segment and analyze for possible future surgery. To create a second segmentation, it would be beneficial to both radiologists and patients in being more confident in their conclusions. We propose using a method performed by radiologists called image segmentation and applying it to machine learning models to prove a better segmentation. Using Mask R-CNN, its ResNet backbone being pre-trained on the RSNA pneumonia detection challenge dataset, we can train a model on the Brats2020 Brain Tumor dataset. Center for Biomedical Image Computing & Analytics provides MRI data on patients with and without brain tumors and the corresponding segmentations. We can see how well the method of image subtraction works by comparing it to models without image subtraction through DICE coefficient (F1 score), recall, and precision on the untouched test set. Our model performed with a DICE coefficient of 0.75 in comparison to 0.69 without image subtraction. To further emphasize the usefulness of image subtraction, we compare our final model to current state-of-the-art models to segment tumors from MRI scans.
