Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks
Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation...
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| Veröffentlicht in: | Scientific reports Jg. 13; H. 1; S. 7109 - 13 |
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| Abstract | Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks. |
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| AbstractList | Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks.Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks. Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks. Abstract Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks. |
| ArticleNumber | 7109 |
| Author | Sawamoto, Kazunobu Sawada, Masato Kimura, Koutarou D. Wen, Chentao Matsumoto, Mami |
| Author_xml | – sequence: 1 givenname: Chentao surname: Wen fullname: Wen, Chentao email: chentao.wen@riken.jp organization: Graduate School of Science, Nagoya City University, RIKEN Center for Biosystems Dynamics Research – sequence: 2 givenname: Mami surname: Matsumoto fullname: Matsumoto, Mami organization: Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Division of Neural Development and Regeneration, National Institute for Physiological Sciences – sequence: 3 givenname: Masato surname: Sawada fullname: Sawada, Masato organization: Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Division of Neural Development and Regeneration, National Institute for Physiological Sciences – sequence: 4 givenname: Kazunobu surname: Sawamoto fullname: Sawamoto, Kazunobu organization: Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Division of Neural Development and Regeneration, National Institute for Physiological Sciences – sequence: 5 givenname: Koutarou D. surname: Kimura fullname: Kimura, Koutarou D. organization: Graduate School of Science, Nagoya City University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37217545$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3390_app14083391 crossref_primary_10_1016_j_compbiomed_2025_109972 crossref_primary_10_1038_s44321_024_00073_7 crossref_primary_10_3390_app14072809 crossref_primary_10_1016_j_bspc_2024_106464 |
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