# Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application

arXiv: https://arxiv.org/abs/2108.09535 · HTML: https://mlsanity.com/paper/2108.09535/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 21 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy](https://mlsanity.com/paper/1809.04430/) (2018)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI](https://mlsanity.com/paper/1903.07988/) (2019)
- [Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation](https://mlsanity.com/paper/2007.10033/) (2020)
- [Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery](https://mlsanity.com/paper/1908.05418/) (2019)
- [Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation](https://mlsanity.com/paper/1808.00244/) (2018)
- [Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation](https://mlsanity.com/paper/1909.02799/) (2019)

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

- [The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study](https://mlsanity.com/paper/2406.08137/) (2024)

## Abstract (arXiv)

We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our segmentation method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow. With our method, we address the relative drawbacks of manual segmentation: high inter-rater contouring variability and high time consumption of the contouring process. The main extension over the existing evaluations is the careful and detailed analysis that could be further generalized on other medical image segmentation tasks. Firstly, we analyze the changes in the inter-rater detection agreement. We show that the segmentation model reduces the ratio of detection disagreements from 0.162 to 0.085 (p < 0.05). Secondly, we show that the model improves the inter-rater contouring agreement from 0.845 to 0.871 surface Dice Score (p < 0.05). Thirdly, we show that the model accelerates the delineation process in between 1.6 and 2.0 times (p < 0.05). Finally, we design the setup of the clinical experiment to either exclude or estimate the evaluation biases, thus preserve the significance of the results. Besides the clinical evaluation, we also summarize the intuitions and practical ideas for building an efficient DL-based model for 3D medical image segmentation.