Detection and segmentation of brain metastases on MRI using 3D‐MedDCNet.
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| Title: | Detection and segmentation of brain metastases on MRI using 3D‐MedDCNet. |
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| Authors: | Wu, Yizhou1,2 (AUTHOR), Li, Yuheng1,3 (AUTHOR), Hu, Mingzhe1,4 (AUTHOR), Chang, Chih‐Wei1 (AUTHOR), Qiu, Richard L. J.1 (AUTHOR), Wang, Tonghe5 (AUTHOR), Shu, Hui‐Kuo1 (AUTHOR), Mao, Hui6 (AUTHOR), Tian, Zhen7 (AUTHOR), Yang, Xiaofeng1,3,4 (AUTHOR) xiaofeng.yang@emory.edu |
| Source: | Medical Physics. Jul2025, Vol. 52 Issue 7, p1-11. 11p. |
| Subjects: | Brain metastasis, Magnetic resonance imaging, Deep learning, Mathematical convolutions, Radiotherapy, Detection algorithms, Image segmentation |
| Abstract: | Background: Brain metastases are a prevalent and serious complication in cancer patients. For effective treatment planning, precise segmentation is required. While conventional standard neural networks have improved automation, they struggle to detect small metastases without increasing false positives. Addressing this challenge is critical for enhancing clinical outcomes in radiotherapy. Purpose: Accurate segmentation of brain metastases in magnetic resonance imaging (MRI) is crucial for clinical decision‐making and treatment planning. Existing deep learning methods, such as nnUNet, have limited power in sensitively detecting small metastatic lesions without increasing false positives. This study proposes a novel deep learning model aimed at addressing this challenge. Methods: We developed 3D‐MedDCNet, a deep learning architecture incorporating deformable convolutions for brain metastasis detection and segmentation. We evaluated its performance against state‐of‐the‐art methods on two datasets: the UCSF Brain Metastases Dataset, comprising 560 MRI scans, and the BraTS‐METS 2023 Dataset, comprising 1,297 MRI scans with expert‐annotated multi‐sequence tumor segmentations. Models were assessed using Sensitivity, Precision, Lesion‐wise Dice, Patient‐wise Dice, and False Positive Rate metrics. The training followed nnUNet's default pipeline, with specific modifications to integrate 3D deformable convolutions (3D‐DCN) at the deepest encoder stage. We also conducted ablation studies to quantify the impact of 3D‐DCN and benchmarked our model against state‐of‐the‐art methods. Results: The developed 3D‐MedDCNet outperformed two state‐of‐the‐art methods across all evaluation metrics. It achieved lesion‐wise Dice scores of 0.80 ± 0.01 (UCSF) and 0.76 ± 0.01 (BraTS), patient‐wise Dice of 0.87 ± 0.01 and 0.82 ± 0.02, sensitivities of 0.84 ± 0.01 and 0.76 ± 0.01, and significantly lower false positive rates of 0.06 ± 0.02 and 0.14 ± 0.01, respectively. Ablation studies confirmed that 3D‐DCN enhances sensitivity while maintaining precision, leading to superior segmentation. Conclusions: 3D‐MedDCNet improved detection sensitivity and segmentation accuracy for brain metastases in MRI over existing state‐of‐the‐art models. This approach enables more reliable automated segmentation and detection of small metastatic lesions for quantifying and staging metastatic diseases, as well as image‐guiding radiation treatment. Future work will focus on validating the model across diverse datasets, exploring foundational models to improve feature representation, and investigating instance‐wise segmentation strategies for enhanced detection and precision. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Background: Brain metastases are a prevalent and serious complication in cancer patients. For effective treatment planning, precise segmentation is required. While conventional standard neural networks have improved automation, they struggle to detect small metastases without increasing false positives. Addressing this challenge is critical for enhancing clinical outcomes in radiotherapy. Purpose: Accurate segmentation of brain metastases in magnetic resonance imaging (MRI) is crucial for clinical decision‐making and treatment planning. Existing deep learning methods, such as nnUNet, have limited power in sensitively detecting small metastatic lesions without increasing false positives. This study proposes a novel deep learning model aimed at addressing this challenge. Methods: We developed 3D‐MedDCNet, a deep learning architecture incorporating deformable convolutions for brain metastasis detection and segmentation. We evaluated its performance against state‐of‐the‐art methods on two datasets: the UCSF Brain Metastases Dataset, comprising 560 MRI scans, and the BraTS‐METS 2023 Dataset, comprising 1,297 MRI scans with expert‐annotated multi‐sequence tumor segmentations. Models were assessed using Sensitivity, Precision, Lesion‐wise Dice, Patient‐wise Dice, and False Positive Rate metrics. The training followed nnUNet's default pipeline, with specific modifications to integrate 3D deformable convolutions (3D‐DCN) at the deepest encoder stage. We also conducted ablation studies to quantify the impact of 3D‐DCN and benchmarked our model against state‐of‐the‐art methods. Results: The developed 3D‐MedDCNet outperformed two state‐of‐the‐art methods across all evaluation metrics. It achieved lesion‐wise Dice scores of 0.80 ± 0.01 (UCSF) and 0.76 ± 0.01 (BraTS), patient‐wise Dice of 0.87 ± 0.01 and 0.82 ± 0.02, sensitivities of 0.84 ± 0.01 and 0.76 ± 0.01, and significantly lower false positive rates of 0.06 ± 0.02 and 0.14 ± 0.01, respectively. Ablation studies confirmed that 3D‐DCN enhances sensitivity while maintaining precision, leading to superior segmentation. Conclusions: 3D‐MedDCNet improved detection sensitivity and segmentation accuracy for brain metastases in MRI over existing state‐of‐the‐art models. This approach enables more reliable automated segmentation and detection of small metastatic lesions for quantifying and staging metastatic diseases, as well as image‐guiding radiation treatment. Future work will focus on validating the model across diverse datasets, exploring foundational models to improve feature representation, and investigating instance‐wise segmentation strategies for enhanced detection and precision. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00942405 |
| DOI: | 10.1002/mp.18001 |
