Pseudo slicer on three dimensional brain tumor segmentation

A constructive way to assist surgeons before performing brain tumor surgery is by visualizing a three-dimensional (3D) MRI image to determine the brain tumor volume when a pre-operative examination. However, the availability of 3D MRI brain tumors is more limited than 2D, especially for meningioma type. Therefore, we attempt to propose a model to construct a pseudo-3D volume of a meningioma tumor from a few 2D MRI slices. In the proposed model, we first generate pseudo slices for each given 2D MRI slice until satisfying 3D MRI construction from interpolation techniques, called pseudoslicer. Next, we segment the pseudo-3D MRIs to capture the 3D meningioma tumor using 3D volume-to-volume generative adversarial networks (3D-V2V-GAN). That is, we call the proposed model 3D-slicer-V2V-GAN or 3DS-V2V-GAN. In this work, the proposed model was evaluated from 27 meningioma patients at a private university hospital in Indonesia. The proposed model outperformed the SOTA model in terms of the Jaccard Index and dice coefficient. Additionally, we have radiologists to affirm the segmentation results in good quality.