# Preoperative brain tumor imaging: models and software for segmentation and standardized reporting

arXiv: https://arxiv.org/abs/2204.14199 · HTML: https://mlsanity.com/paper/2204.14199/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 68 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.10030/) (2019)
- [Common Limitations of Image Processing Metrics: A Picture Story](https://mlsanity.com/paper/2104.05642/) (2021)
- [SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions](https://mlsanity.com/paper/1912.12355/) (2019)
- [Extending nn-UNet for brain tumor segmentation](https://mlsanity.com/paper/2112.04653/) (2021)
- [Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI](https://mlsanity.com/paper/1903.07988/) (2019)
- [GBM Volumetry using the 3D Slicer Medical Image Computing Platform](https://mlsanity.com/paper/1303.0964/) (2013)
- [Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms](https://mlsanity.com/paper/2101.07715/) (2021)
- [Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture](https://mlsanity.com/paper/2010.07002/) (2020)

## Built on by (most cited first, 9 of 9)

- [Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge](https://mlsanity.com/paper/2405.09787/) (2024)
- [Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks](https://mlsanity.com/paper/2304.08881/) (2023)
- [Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting](https://mlsanity.com/paper/2305.14351/) (2023)
- [Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations](https://mlsanity.com/paper/2411.14017/) (2024)
- [Exploring the Capabilities and Limitations of Large Language Models for Radiation Oncology Decision Support](https://mlsanity.com/paper/2501.02346/) (2025)
- [Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation](https://mlsanity.com/paper/2405.10870/) (2024)
- [Automatic and standardized surgical reporting for central nervous system tumors](https://mlsanity.com/paper/2508.08916/) (2025)
- [Comparison of different segmentation algorithms on brain volume and fractal dimension in infant brain MRIs](https://mlsanity.com/paper/2512.12222/) (2025)
- [A unified FLAIR hyperintensity segmentation model for various CNS tumor types and acquisition time points](https://mlsanity.com/paper/2512.17566/) (2025)

## Abstract (arXiv)

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports represents a major hurdle. In this study, we investigate glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80% and 90%, patient-wise recall between 88% and 98%, and patient-wise precision around 95%. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16 to 54 seconds depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5 to 15 minutes are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.