# Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/metrics-to-evaluate-the-performance-of-auto-segmentation-for-radiation-treatment/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Michael V. Sherer,Diana Lin,Sharif Elguindi,Simon Duke,Li Tee Tan,Jon Cacicedo,Max Dahele,Erin F. Gillespie |
| Citations | 237 |
| DOI | 10.1016/j.radonc.2021.05.003 |
| Fields | Engineering,Medicine,Physics and Astronomy |
| Open Access | true |
| OA Status | green |
| OA URL | https://www.ncbi.nlm.nih.gov/pmc/articles/9444281 |
| OpenAlex ID | https://openalex.org/W3160604201 |
| PMID | 33984348 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Jon Cacicedo](https://scholariq.org/researchers/jon-cacicedo/)

## Paper journal

- [Radiotherapy and Oncology](https://scholariq.org/journals/radiotherapy-and-oncology/)

## Paper primary topic

- [Advanced Radiotherapy Techniques](https://scholariq.org/topics/advanced-radiotherapy-techniques/)

## Paper topics

- [Advanced Radiotherapy Techniques](https://scholariq.org/topics/advanced-radiotherapy-techniques/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [Advanced X-ray and CT Imaging](https://scholariq.org/topics/advanced-x-ray-and-ct-imaging/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.