# Multi-Modality Abdominal Multi-Organ Segmentation with Deep Supervised 3D Segmentation Model

arXiv: https://arxiv.org/abs/2208.12041 · HTML: https://mlsanity.com/paper/2208.12041/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 3 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Optimized U-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2110.03352/) (2021)

## Built on by (most cited first, 1 of 1)

- [MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures](https://mlsanity.com/paper/2402.01031/) (2024)

## Abstract (arXiv)

To promote the development of medical image segmentation technology, AMOS, a large-scale abdominal multi-organ dataset for versatile medical image segmentation, is provided and AMOS 2022 challenge is held by using the dataset. In this report, we present our solution for the AMOS 2022 challenge. We employ residual U-Net with deep super vision as our base model. The experimental results show that the mean scores of Dice similarity coefficient and normalized surface dice are 0.8504 and 0.8476 for CT only task and CT/MRI task, respectively.