# CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/1907.07677 · HTML: https://mlsanity.com/paper/1907.07677/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

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

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model](https://mlsanity.com/paper/2309.00810/) (2023)
- [EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment](https://mlsanity.com/paper/2312.08548/) (2023)
- [FRIH: Fine-grained Region-aware Image Harmonization](https://mlsanity.com/paper/2205.06448/) (2022)
- [Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images](https://mlsanity.com/paper/2205.15941/) (2022)
- [Anatomical Region Recognition and Real-time Bone Tracking Methods by Dynamically Decoding A-Mode Ultrasound Signals](https://mlsanity.com/paper/2405.19542/) (2024)

## Abstract (arXiv)

This paper proposes a novel cascaded U-Net for brain tumor segmentation. Inspired by the distinct hierarchical structure of brain tumor, we design a cascaded deep network framework, in which the whole tumor is segmented firstly and then the tumor internal substructures are further segmented. Considering that the increase of the network depth brought by cascade structures leads to a loss of accurate localization information in deeper layers, we construct many skip connections to link features at the same resolution and transmit detailed information from shallow layers to the deeper layers. Then we present a loss weighted sampling (LWS) scheme to eliminate the issue of imbalanced data during training the network. Experimental results on BraTS 2017 data show that our architecture framework outperforms the state-of-the-art segmentation algorithms, especially in terms of segmentation sensitivity.