# Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes

arXiv: https://arxiv.org/abs/1806.06769 · HTML: https://mlsanity.com/paper/1806.06769/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching](https://mlsanity.com/paper/2306.11925/) (2023)
- [Kidney abnormality segmentation in thorax-abdomen CT scans](https://mlsanity.com/paper/2309.03383/) (2023)
- [Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging](https://mlsanity.com/paper/2311.13319/) (2023)
- [CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images](https://mlsanity.com/paper/2108.06669/) (2021)
- [CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation](https://mlsanity.com/paper/2208.05241/) (2022)
- [EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation](https://mlsanity.com/paper/2106.04130/) (2021)

## Abstract (arXiv)

Semantic image segmentation plays an important role in modeling patient-specific anatomy. We propose a convolution neural network, called Kid-Net, along with a training schema to segment kidney vessels: artery, vein and collecting system. Such segmentation is vital during the surgical planning phase in which medical decisions are made before surgical incision. Our main contribution is developing a training schema that handles unbalanced data, reduces false positives and enables high-resolution segmentation with a limited memory budget. These objectives are attained using dynamic weighting, random sampling and 3D patch segmentation. Manual medical image annotation is both time-consuming and expensive. Kid-Net reduces kidney vessels segmentation time from matter of hours to minutes. It is trained end-to-end using 3D patches from volumetric CT-images. A complete segmentation for a 512x512x512 CT-volume is obtained within a few minutes (1-2 mins) by stitching the output 3D patches together. Feature down-sampling and up-sampling are utilized to achieve higher classification and localization accuracies. Quantitative and qualitative evaluation results on a challenging testing dataset show Kid-Net competence.