A deep neural network-based end-to-end 3D medical abdominal segmentation and reconstruction model

In recent years, medical imaging-based disease detection and diagnosis have become a mainstream practice in the medical industry. Various computer-aided reconstruction models assist medical practitioners to detect tumors and polyps, thereby diagnosing them more effectively. Deep learning is an emerging computer vision method that has seen tremendous growth in this field, but labeling data in medical imaging is difficult and inefficient, making it extremely expensive to train for supervised learning methods. For unsupervised learning models, the intrinsic logic limits its effectiveness on unlabeled data. In this paper, in order to address this challenge, traditional geometric 3D model reconstruction methods are combined with the latest supervised deep learning models to propose a novel GeoVNet, a semi-supervised model. The proposed GeoVNet model is provided to the intestinal dataset for reconstruction and segmentation. Finally, the experimental evaluations are conducted for various performance metrics, and the precision rate attained is 98.16%, recall rate achieved is 97.9%, IoU as well as dice coefficient obtained are 83.5% as well as 90.9%, respectively. This shows that proposed approach outperforms unlabeled multi-device MRI intestinal imaging methods, and common models such as 3DUNet and DeepLabV3. In addition to this, the ablation analysis is conducted in order to validate and determine the effect of model hyperparameters on segmentation results.