# Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI

arXiv: https://arxiv.org/abs/1903.07988 · HTML: https://mlsanity.com/paper/1903.07988/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 30 of 30)

- [ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image](https://mlsanity.com/paper/2312.07381/) (2023)
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- [Primus: Enforcing Attention Usage for 3D Medical Image Segmentation](https://mlsanity.com/paper/2503.01835/) (2025)
- [3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers](https://mlsanity.com/paper/2310.07781/) (2023)
- [Deep learning for brain metastasis detection and segmentation in longitudinal MRI data](https://mlsanity.com/paper/2112.11833/) (2021)
- [Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification](https://mlsanity.com/paper/2204.13591/) (2022)
- [MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance](https://mlsanity.com/paper/2412.15058/) (2024)
- [LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging](https://mlsanity.com/paper/2502.20985/) (2025)
- [Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI](https://mlsanity.com/paper/2302.00517/) (2023)
- [Preoperative brain tumor imaging: models and software for segmentation and standardized reporting](https://mlsanity.com/paper/2204.14199/) (2022)
- [The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset](https://mlsanity.com/paper/2304.07248/) (2023)
- [A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning](https://mlsanity.com/paper/2506.14432/) (2025)
- [MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentation](https://mlsanity.com/paper/2512.17774/) (2025)
- [3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge](https://mlsanity.com/paper/2403.15735/) (2024)
- [Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting](https://mlsanity.com/paper/2305.14351/) (2023)
- [Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation](https://mlsanity.com/paper/2511.17146/) (2025)
- [The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection](https://mlsanity.com/paper/2509.15947/) (2025)
- [Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application](https://mlsanity.com/paper/2108.09535/) (2021)
- [Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI using Noisy Student-based Training](https://mlsanity.com/paper/2111.05959/) (2021)
- [Exploring the Capabilities and Limitations of Large Language Models for Radiation Oncology Decision Support](https://mlsanity.com/paper/2501.02346/) (2025)
- [Topology-Driven Transferability Estimation for 3D Medical Vision Foundation Models](https://mlsanity.com/paper/2607.04199/) (2026)
- [Pre-examinations Improve Automated Metastases Detection on Cranial MRI](https://mlsanity.com/paper/2403.08280/) (2024)
- [Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation](https://mlsanity.com/paper/2405.10870/) (2024)
- [Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation](https://mlsanity.com/paper/2504.06741/) (2025)
- [Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI](https://mlsanity.com/paper/2511.18208/) (2025)
- [Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions](https://mlsanity.com/paper/2604.00397/) (2026)
- [Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection](https://mlsanity.com/paper/2605.01563/) (2026)
- [SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images](https://mlsanity.com/paper/2607.22332/) (2026)

## Abstract (arXiv)

Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multi-sequence 3D imaging. This study demonstrates automated detection and segmentation of brain metastases on multi-sequence MRI using a deep learning approach based on a fully convolution neural network (CNN). In this retrospective study, a total of 156 patients with brain metastases from several primary cancers were included. Pre-therapy MR images (1.5T and 3T) included pre- and post-gadolinium T1-weighted 3D fast spin echo, post-gadolinium T1-weighted 3D axial IR-prepped FSPGR, and 3D fluid attenuated inversion recovery. The ground truth was established by manual delineation by two experienced neuroradiologists. CNN training/development was performed using 100 and 5 patients, respectively, with a 2.5D network based on a GoogLeNet architecture. The results were evaluated in 51 patients, equally separated into those with few (1-3), multiple (4-10), and many (>10) lesions. Network performance was evaluated using precision, recall, Dice/F1 score, and ROC-curve statistics. For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis. The area under the ROC-curve (AUC), averaged across all patients, was 0.98. The AUC in the subgroups was 0.99, 0.97, and 0.97 for patients having 1-3, 4-10, and >10 metastases, respectively. Using an average optimal probability threshold determined by the development set, precision, recall, and Dice-score were 0.79, 0.53, and 0.79, respectively. At the same probability threshold, the network showed an average false positive rate of 8.3/patient (no lesion-size limit) and 3.4/patient (10 mm3 lesion size limit). In conclusion, a deep learning approach using multi-sequence MRI can aid in the detection and segmentation of brain metastases.