# Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images

arXiv: https://arxiv.org/abs/1804.10484 · HTML: https://mlsanity.com/paper/1804.10484/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI](https://mlsanity.com/paper/2203.15163/) (2022)
- [Self-supervised Tumor Segmentation through Layer Decomposition](https://mlsanity.com/paper/2109.03230/) (2021)
- [Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data](https://mlsanity.com/paper/2112.12665/) (2021)
- [A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2309.03906/) (2023)
- [Kidney abnormality segmentation in thorax-abdomen CT scans](https://mlsanity.com/paper/2309.03383/) (2023)
- [Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans](https://mlsanity.com/paper/2405.03713/) (2024)
- [SIGAN: A Novel Image Generation Method for Solar Cell Defect Segmentation and Augmentation](https://mlsanity.com/paper/2104.04953/) (2021)
- [Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentation](https://mlsanity.com/paper/2406.18327/) (2024)
- [Universal Medical Image Representation Learning with Compositional Decoders](https://mlsanity.com/paper/2409.19890/) (2024)
- [Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions](https://mlsanity.com/paper/2107.11517/) (2021)
- [SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations](https://mlsanity.com/paper/2301.07074/) (2023)
- [CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2505.18958/) (2025)

## Abstract (arXiv)

Due to the irregular motion, similar appearance and diverse shape, accurate segmentation of kidney tumor in CT images is a difficult and challenging task. To this end, we present a novel automatic segmentation method, termed as Crossbar-Net, with the goal of accurate segmenting the kidney tumors. Firstly, considering that the traditional learning-based segmentation methods normally employ either whole images or squared patches as the training samples, we innovatively sample the orthogonal non-squared patches (namely crossbar patches), to fully cover the whole kidney tumors in either horizontal or vertical directions. These sampled crossbar patches could not only represent the detailed local information of kidney tumor as the traditional patches, but also describe the global appearance from either horizontal or vertical direction using contextual information. Secondly, with the obtained crossbar patches, we trained a convolutional neural network with two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded manner, to integrate the segmentation results from two directions (i.e., horizontal and vertical). This cascaded training strategy could effectively guarantee the consistency between sub-models, by feeding each other with the most difficult samples, for a better segmentation. In the experiment, we evaluate our method on a real CT kidney tumor dataset, collected from 94 different patients including 3,500 images. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior results of our method on dice ratio score, true positive fraction, centroid distance and Hausdorff distance. Moreover, we have extended our crossbar-net to a different task: cardiac segmentation, showing the promising results for the better generalization.