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ilastik pipeline  (Oxford Instruments)
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Combined <t>Ilastik®,</t> ImageJ, and Imaris® processing pipeline. A) Individual z-slices were selected from the 3D LSM image that represented the inter-condition, inter-replicate, and intra-sample variability. The Ilastik® classifier was trained to select “nerve” and “not nerve” or background data. Post-training, Ilastik® segmentation was applied to all the z-slices in each 3D image. Samples were then processed in ImageJ to generate binary nerve masks. Binary nerve masks were imported back into Imaris® for 3D segmentation, and re-masking for statistical analyses. B) Comparison of Imaris® intensity-based segmentation and 3D reconstruction with Ilastik® machine learning segmentation and Imaris® 3D reconstruction. Scale bar = 100 μm. C) Comparison of Ilastik® machine learning segmentation with one, two, or four training slices. One and two training slices were intra-sample slices, while four slices combined two sets of intra-sample slices from two independent replicates. Scale bar = 100 μm.
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nmeth 2019 scr 003070 ilastik 1 3 2 berg  (Oxford Instruments)
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Combined <t>Ilastik®,</t> ImageJ, and Imaris® processing pipeline. A) Individual z-slices were selected from the 3D LSM image that represented the inter-condition, inter-replicate, and intra-sample variability. The Ilastik® classifier was trained to select “nerve” and “not nerve” or background data. Post-training, Ilastik® segmentation was applied to all the z-slices in each 3D image. Samples were then processed in ImageJ to generate binary nerve masks. Binary nerve masks were imported back into Imaris® for 3D segmentation, and re-masking for statistical analyses. B) Comparison of Imaris® intensity-based segmentation and 3D reconstruction with Ilastik® machine learning segmentation and Imaris® 3D reconstruction. Scale bar = 100 μm. C) Comparison of Ilastik® machine learning segmentation with one, two, or four training slices. One and two training slices were intra-sample slices, while four slices combined two sets of intra-sample slices from two independent replicates. Scale bar = 100 μm.
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ilastik  (MathWorks Inc)
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fiji ilastik  (Oxford Instruments)
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Combined <t>Ilastik®,</t> ImageJ, and Imaris® processing pipeline. A) Individual z-slices were selected from the 3D LSM image that represented the inter-condition, inter-replicate, and intra-sample variability. The Ilastik® classifier was trained to select “nerve” and “not nerve” or background data. Post-training, Ilastik® segmentation was applied to all the z-slices in each 3D image. Samples were then processed in ImageJ to generate binary nerve masks. Binary nerve masks were imported back into Imaris® for 3D segmentation, and re-masking for statistical analyses. B) Comparison of Imaris® intensity-based segmentation and 3D reconstruction with Ilastik® machine learning segmentation and Imaris® 3D reconstruction. Scale bar = 100 μm. C) Comparison of Ilastik® machine learning segmentation with one, two, or four training slices. One and two training slices were intra-sample slices, while four slices combined two sets of intra-sample slices from two independent replicates. Scale bar = 100 μm.
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Combined Ilastik®, ImageJ, and Imaris® processing pipeline. A) Individual z-slices were selected from the 3D LSM image that represented the inter-condition, inter-replicate, and intra-sample variability. The Ilastik® classifier was trained to select “nerve” and “not nerve” or background data. Post-training, Ilastik® segmentation was applied to all the z-slices in each 3D image. Samples were then processed in ImageJ to generate binary nerve masks. Binary nerve masks were imported back into Imaris® for 3D segmentation, and re-masking for statistical analyses. B) Comparison of Imaris® intensity-based segmentation and 3D reconstruction with Ilastik® machine learning segmentation and Imaris® 3D reconstruction. Scale bar = 100 μm. C) Comparison of Ilastik® machine learning segmentation with one, two, or four training slices. One and two training slices were intra-sample slices, while four slices combined two sets of intra-sample slices from two independent replicates. Scale bar = 100 μm.

Journal: Bone Reports

Article Title: A novel workflow for 3D imaging and spatial analysis of nerves in bone ☆

doi: 10.1016/j.bonr.2025.101881

Figure Lengend Snippet: Combined Ilastik®, ImageJ, and Imaris® processing pipeline. A) Individual z-slices were selected from the 3D LSM image that represented the inter-condition, inter-replicate, and intra-sample variability. The Ilastik® classifier was trained to select “nerve” and “not nerve” or background data. Post-training, Ilastik® segmentation was applied to all the z-slices in each 3D image. Samples were then processed in ImageJ to generate binary nerve masks. Binary nerve masks were imported back into Imaris® for 3D segmentation, and re-masking for statistical analyses. B) Comparison of Imaris® intensity-based segmentation and 3D reconstruction with Ilastik® machine learning segmentation and Imaris® 3D reconstruction. Scale bar = 100 μm. C) Comparison of Ilastik® machine learning segmentation with one, two, or four training slices. One and two training slices were intra-sample slices, while four slices combined two sets of intra-sample slices from two independent replicates. Scale bar = 100 μm.

Article Snippet: Comparing the Ilastik® pipeline with Imaris® intensity-based segmentation, we demonstrated improved segmentation of nerve-like structures, with reduced segmentation of non-nerve background ( B).

Techniques: Comparison

Whole mount calvarial defect nerve image processing and analysis. A) Maximum intensity projection (MIP) image of TUBB3+ nerves in the calvarial defects 1 week, 2 weeks, and 4 weeks after 4-mm critical-sized calvarial defect injury. B) Binary Ilastik® masks prior to ImageJ processing. C) Imaris® absolute segmentation used for nerve masking. D) Resulting nerve mask in Imaris®. E) Radial density plots for the calvarial defect at 1 week, 2 week, 4 weeks, and 8 weeks post-injury. Color bar indicates the 2D nerve density (μm 3 /μm 3 ) per radial region. Scale bar = 300 μm.

Journal: Bone Reports

Article Title: A novel workflow for 3D imaging and spatial analysis of nerves in bone ☆

doi: 10.1016/j.bonr.2025.101881

Figure Lengend Snippet: Whole mount calvarial defect nerve image processing and analysis. A) Maximum intensity projection (MIP) image of TUBB3+ nerves in the calvarial defects 1 week, 2 weeks, and 4 weeks after 4-mm critical-sized calvarial defect injury. B) Binary Ilastik® masks prior to ImageJ processing. C) Imaris® absolute segmentation used for nerve masking. D) Resulting nerve mask in Imaris®. E) Radial density plots for the calvarial defect at 1 week, 2 week, 4 weeks, and 8 weeks post-injury. Color bar indicates the 2D nerve density (μm 3 /μm 3 ) per radial region. Scale bar = 300 μm.

Article Snippet: Comparing the Ilastik® pipeline with Imaris® intensity-based segmentation, we demonstrated improved segmentation of nerve-like structures, with reduced segmentation of non-nerve background ( B).

Techniques:

Gold standard evaluation. A) Gold standard and Ilastik® overlay on TUBB3+, VAChT+, and Baf53b + nerves in calvarial defects, metatarsal bones, and knee joints, respectively. Scale bar is 100 μm. B) Percent overlap calculation, between gold standard and Ilastik® data.

Journal: Bone Reports

Article Title: A novel workflow for 3D imaging and spatial analysis of nerves in bone ☆

doi: 10.1016/j.bonr.2025.101881

Figure Lengend Snippet: Gold standard evaluation. A) Gold standard and Ilastik® overlay on TUBB3+, VAChT+, and Baf53b + nerves in calvarial defects, metatarsal bones, and knee joints, respectively. Scale bar is 100 μm. B) Percent overlap calculation, between gold standard and Ilastik® data.

Article Snippet: Comparing the Ilastik® pipeline with Imaris® intensity-based segmentation, we demonstrated improved segmentation of nerve-like structures, with reduced segmentation of non-nerve background ( B).

Techniques: