Vol. 12 No. 1 (2024): Synchronous endovascular management of post PCNL concurrent pseudoaneurysm and AV fistula & Automatic Joint Teeth Segmentation in Panoramic Dental Images using Mask Recurrent Convolutional Neural Networks with Residual Feature Extraction: Can it be useful in Oral Cancer Diagnosis and Management?
Synchronous endovascular management of post PCNL concurrent pseudoaneurysm and AV fistula
Percutaneous nephrolithotomy (PCNL) is the standard treatment procedure for large stones associated with complications like pseudoaneurysm and arteriovenous fistula with their incidence being < 1%. A post-PCNL case with left flank pain and delayed haematuria presented with macroscopic haematuria and depleting haemoglobin levels. CT angiography with 3-D reconstruction was used for diagnosing and planning of treatment. The patient was successfully treated with super selective angioembolization (SAE) using peripheral coils while preserving the kidney's remaining vascularization. Early diagnosis and active endovascular treatment using angioembolization techniques can be life-saving and resulting in minimal post-procedure complications and early recovery.
Automatic Joint Teeth Segmentation in Panoramic Dental Images using Mask Recurrent Convolutional Neural Networks with Residual Feature Extraction: Can it be useful in Oral Cancer Diagnosis and Management?
Introduction: Panoramic dental images gives an in-depth understanding of the tooth structure, both lower and upper jaws, and surrounding structures throughout the cavity in our mouth. The Panoramic dental images provided have significance for dental diagnostics since they aid in the detection of an array of dental disorders, including oral cancer. We propose a novel approach to automatic joint teeth segmentation using the pioneer Mask Recurrent Convolutional Neural Network (MRCNN) model for dental image segmentation.
Material and Methods: In this study, a sequence of residual blocks are used to construct a 62-layer feature extraction network in lieu of ResNet50/101 in MRCNN. To evaluate the efficacy of our method, the UFBA-UESC and Tufts dental image dataset (2500 panoramic dental x-rays) were utilised. 252 x-rays were used in test set, rest of the x-rays were utilised as training (1800 images) and validation datasets (448 images) in ratio of 8:2 of the modified MRCNN model.
Results: Modified MRCNN achieved the final training and validation accuracies as 99.67% and 98.94%, respectively. The achieved accuracy of Dice coefficient (97.8%), Intersection over Union, (98.67%), and Pixel Accuracy (96.53%) respectively over the whole dataset. We also compare the performance of proposed model and other well established networks such as FPN, UNet, PSPNet, and DeepLabV3. The Modified MRCNN provides better results segmenting any two teeth which are close to each other.
Conclusion: Our proposed method will serve as a valuable tool for automatic segmentation of individual teeth for medical management. This current method leads to higher accuracy and precision. Segmented images can be used to evaluate periodic changes, providing valuable data for assessing the progression of oral cancer and the efficacy of management. Future research should focus on developing less complex, lightweight, and faster vision models while maintaining high accuracy
