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Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.

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Title: Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.
Authors: Spaanderman, Douwe J.1 (AUTHOR) d.spaanderman@erasmusmc.nl, Starmans, Martijn P. A.1 (AUTHOR), van Erp, Gonnie C. M.1 (AUTHOR), Hanff, David F.1 (AUTHOR), Sluijter, Judith H.1 (AUTHOR), Schut, Anne-Rose W.2,3 (AUTHOR), van Leenders, Geert J. L. H.4 (AUTHOR), Verhoef, Cornelis2 (AUTHOR), Grünhagen, Dirk J.2 (AUTHOR), Niessen, Wiro J.5 (AUTHOR), Visser, Jacob J.1 (AUTHOR), Klein, Stefan1 (AUTHOR)
Source: European Radiology. May2025, Vol. 35 Issue 5, p2736-2745. 10p.
Subjects: Soft tissue tumors, Deep learning, Computer-assisted image analysis (Medicine), Magnetic resonance imaging, Image segmentation
Abstract: Background: Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par. Purpose: To develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI. Material and methods: The interactive method requires the user to click six points near the tumor's extreme boundaries in the image. These six points are transformed into a distance map and serve, with the image, as input for a convolutional neural network. A multi-center public dataset with 514 patients and nine STT phenotypes in seven anatomical locations, with CT or T1-weighted MRI, was used for training and internal validation. For external validation, another public dataset was employed, which included five unseen STT phenotypes in extremities on CT, T1-weighted MRI, and T2-weighted fat-saturated (FS) MRI. Results: Internal validation resulted in a dice similarity coefficient (DSC) of 0.85 ± 0.11 (mean ± standard deviation) for CT and 0.84 ± 0.12 for T1-weighted MRI. External validation resulted in DSCs of 0.81 ± 0.08 for CT, 0.84 ± 0.09 for T1-weighted MRI, and 0.88 ± 0.08 for T2-weighted FS MRI. Volumetric measurements showed consistent replication with low error internally (volume: 1 ± 28 mm3, r = 0.99; diameter: − 6 ± 14 mm, r = 0.90) and externally (volume: − 7 ± 23 mm3, r = 0.96; diameter: − 3 ± 6 mm, r = 0.99). Interactive segmentation time was considerably shorter (CT: 364 s, T1-weighted MRI: 258s) than manual segmentation (CT: 1639s, T1-weighted MRI: 1895s). Conclusion: The minimally interactive segmentation method effectively segments STT phenotypes on CT and MRI, with robust generalization to unseen phenotypes and imaging modalities. Key Points: QuestionCan this deep learning-based method segment soft-tissue tumors faster than can be done manually and more accurately than other automatic methods? Findings The minimally interactive segmentation method achieved accurate segmentation results in internal and external validation, and generalized well across soft-tissue tumor phenotypes and imaging modalities. Clinical relevance This minimally interactive deep learning-based segmentation method could reduce the burden of manual segmentation, facilitate the integration of imaging-based biomarkers (e.g., radiomics) into clinical practice, and provide a fast, semi-automatic solution for volume and diameter measurements (e.g., RECIST). [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.
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  Data: <searchLink fieldCode="AR" term="%22Spaanderman%2C+Douwe+J%2E%22">Spaanderman, Douwe J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> d.spaanderman@erasmusmc.nl</i><br /><searchLink fieldCode="AR" term="%22Starmans%2C+Martijn+P%2E+A%2E%22">Starmans, Martijn P. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22van+Erp%2C+Gonnie+C%2E+M%2E%22">van Erp, Gonnie C. M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hanff%2C+David+F%2E%22">Hanff, David F.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sluijter%2C+Judith+H%2E%22">Sluijter, Judith H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schut%2C+Anne-Rose+W%2E%22">Schut, Anne-Rose W.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22van+Leenders%2C+Geert+J%2E+L%2E+H%2E%22">van Leenders, Geert J. L. H.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Verhoef%2C+Cornelis%22">Verhoef, Cornelis</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Grünhagen%2C+Dirk+J%2E%22">Grünhagen, Dirk J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Niessen%2C+Wiro+J%2E%22">Niessen, Wiro J.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Visser%2C+Jacob+J%2E%22">Visser, Jacob J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Klein%2C+Stefan%22">Klein, Stefan</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. May2025, Vol. 35 Issue 5, p2736-2745. 10p.
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– Name: Abstract
  Label: Abstract
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  Data: Background: Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par. Purpose: To develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI. Material and methods: The interactive method requires the user to click six points near the tumor's extreme boundaries in the image. These six points are transformed into a distance map and serve, with the image, as input for a convolutional neural network. A multi-center public dataset with 514 patients and nine STT phenotypes in seven anatomical locations, with CT or T1-weighted MRI, was used for training and internal validation. For external validation, another public dataset was employed, which included five unseen STT phenotypes in extremities on CT, T1-weighted MRI, and T2-weighted fat-saturated (FS) MRI. Results: Internal validation resulted in a dice similarity coefficient (DSC) of 0.85 ± 0.11 (mean ± standard deviation) for CT and 0.84 ± 0.12 for T1-weighted MRI. External validation resulted in DSCs of 0.81 ± 0.08 for CT, 0.84 ± 0.09 for T1-weighted MRI, and 0.88 ± 0.08 for T2-weighted FS MRI. Volumetric measurements showed consistent replication with low error internally (volume: 1 ± 28 mm3, r = 0.99; diameter: − 6 ± 14 mm, r = 0.90) and externally (volume: − 7 ± 23 mm3, r = 0.96; diameter: − 3 ± 6 mm, r = 0.99). Interactive segmentation time was considerably shorter (CT: 364 s, T1-weighted MRI: 258s) than manual segmentation (CT: 1639s, T1-weighted MRI: 1895s). Conclusion: The minimally interactive segmentation method effectively segments STT phenotypes on CT and MRI, with robust generalization to unseen phenotypes and imaging modalities. Key Points: QuestionCan this deep learning-based method segment soft-tissue tumors faster than can be done manually and more accurately than other automatic methods? Findings The minimally interactive segmentation method achieved accurate segmentation results in internal and external validation, and generalized well across soft-tissue tumor phenotypes and imaging modalities. Clinical relevance This minimally interactive deep learning-based segmentation method could reduce the burden of manual segmentation, facilitate the integration of imaging-based biomarkers (e.g., radiomics) into clinical practice, and provide a fast, semi-automatic solution for volume and diameter measurements (e.g., RECIST). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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