Z-Net: an Anisotropic 3D DCNN for Medical CT Volume Segmentation
This addresses discontinuity and class-imbalance issues in medical CT segmentation for robot-assisted minimally invasive surgery, but is an incremental improvement over existing 3D DCNNs.
The paper tackled the problem of memory-intensive 3D DCNNs for CT volume segmentation by introducing Z-Net, which uses anisotropic spatial separable convolutions to preserve full field-of-view in XY planes, resulting in improved IoU up to 12.6%.
Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS). 3D Deep Convolutional Neural Network (DCNN) is a viable solution for this task, but is memory intensive. Small isotropic patches are cropped from the original and large CT volume to mitigate this issue in practice, but it may cause discontinuities between the adjacent patches and severe class-imbalances within individual sub-volumes. This paper presents a new 3D DCNN framework, namely Z-Net, to tackle the discontinuity and class-imbalance issue by preserving a full field-of-view of the objects in the XY planes using anisotropic spatial separable convolutions. The proposed Z-Net can be seamlessly integrated into existing 3D DCNNs with isotropic convolutions such as 3D U-Net and V-Net, with improved volume segmentation Intersection over Union (IoU) - up to $12.6\%$. Detailed validation of Z-Net is provided for CT aortic, liver and lung segmentation, demonstrating the effectiveness and practical value of Z-Net for intra-operative 3D navigation in robot-assisted MIS.