# Novel coronavirus pneumonia lesion segmentation in CT images

arXiv: https://arxiv.org/abs/2110.12827 · HTML: https://mlsanity.com/paper/2110.12827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018)
- [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019)
- [Label-Free Segmentation of COVID-19 Lesions in Lung CT](https://mlsanity.com/paper/2009.06456/) (2020)
- [A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images](https://mlsanity.com/paper/2007.02180/) (2020)
- [Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19](https://mlsanity.com/paper/2004.03399/) (2020)
- [Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning](https://mlsanity.com/paper/2103.00780/) (2021)
- [Bifurcated Autoencoder for Segmentation of COVID-19 Infected Regions in CT Images](https://mlsanity.com/paper/2011.00631/) (2020)

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

- [Pulmonary Fissure Segmentation in CT Images Based on ODoS Filter and Shape Features](https://mlsanity.com/paper/2201.09163/) (2022)

## Abstract (arXiv)

Background: The 2019 novel coronavirus disease (COVID-19) has been spread widely in the world, causing a huge threat to people's living environment. Objective: Under computed tomography (CT) imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Considering the fact that a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtained, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract COVID-19 lesion features effectively, which may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results shown that the proposed mehtod has a perfect performance in COVID-19 lesion segmentation.