# Deep learning for brain metastasis detection and segmentation in longitudinal MRI data arXiv: https://arxiv.org/abs/2112.11833 ยท HTML: https://mlsanity.com/paper/2112.11833/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 7 of its 44 references that are in the map. - First posted on arXiv 22 Dec 2021. ## Builds on (most cited first, 7 of 7) - [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017) - [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) - [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020) - [Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI](https://mlsanity.com/paper/1903.07988/) (2019) - [Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery](https://mlsanity.com/paper/1908.05418/) (2019) - [Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI](https://mlsanity.com/paper/1908.04701/) (2019) ## Built on by (most cited first, 16 of 16) - [The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning](https://mlsanity.com/paper/2304.07875/) (2023) - [Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification](https://mlsanity.com/paper/2204.13591/) (2022) - [Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology](https://mlsanity.com/paper/2408.10715/) (2024) - [A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration](https://mlsanity.com/paper/2309.17192/) (2023) - [Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology](https://mlsanity.com/paper/2304.11957/) (2023) - [Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding](https://mlsanity.com/paper/2306.14596/) (2023) - [FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation](https://mlsanity.com/paper/2410.22057/) (2024) - [Exploring the Capabilities and Limitations of Large Language Models for Radiation Oncology Decision Support](https://mlsanity.com/paper/2501.02346/) (2025) - [BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis](https://mlsanity.com/paper/2507.09036/) (2025) - [Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning](https://mlsanity.com/paper/2302.08802/) (2023) - [Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation](https://mlsanity.com/paper/2405.10870/) (2024) - [Benchmarking GPT-5 in Radiation Oncology: Measurable Gains, but Persistent Need for Expert Oversight](https://mlsanity.com/paper/2508.21777/) (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) - [Improving Deep Learning-Based Target Volume Auto-Delineation for Adaptive MR-Guided Radiotherapy in Head and Neck Cancer: Impact of a Volume-Aware Dice Loss](https://mlsanity.com/paper/2604.10130/) (2026) - [Deep Learning for Longitudinal Medical Imaging: A Scoping Review](https://mlsanity.com/paper/2610.08838/) (2026) ## Abstract (arXiv) Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in radiation therapy. To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels. As sensitivity and precision are always a trade-off in a metastasis level, either a high sensitivity or a high precision can be achieved by adjusting the weights in the VSS loss without decline in dice score coefficient for segmented metastases. To reduce metastasis-like structures being detected as false positive metastases, a temporal prior volume is proposed as an additional input of DeepMedic. The modified network is called DeepMedic+ for distinction. Our proposed VSS loss improves the sensitivity of brain metastasis detection for DeepMedic, increasing the sensitivity from 85.3% to 97.5%. Alternatively, it improves the precision from 69.1% to 98.7%. Comparing DeepMedic+ with DeepMedic with the same VSS loss, 44.4% of the false positive metastases are reduced in the high sensitivity model and the precision reaches 99.6% for the high specificity model. The mean dice coefficient for all metastases is about 0.81. With the ensemble of the high sensitivity and high specificity models, on average only 1.5 false positive metastases per patient needs further check, while the majority of true positive metastases are confirmed. The ensemble learning is able to distinguish high confidence true positive metastases from metastases candidates that require special expert review or further follow-up, being particularly well-fit to the requirements of expert support in real clinical practice.