Journal: Bone Reports

Article Title: A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets

doi: 10.1016/j.bonr.2022.101167

Figure Lengend Snippet: Watershed-based semi-automated segmentation of murine hindpaw micro-CT datasets. 3D rendering of the μCT data was performed, and representative images of the hindpaw bones are shown to demonstrate the semi-automated segmentation method. This method utilizes the original μCT data after application of a three-dimensional median filter for edge detection (A), a binary mask with a set threshold of 2500 HU (B), and bone-specific watershed seeds (eroded versions of the bone-specific labels) (C). Together, these inputs generate bone-specific segmentations that expand to the full volume of the bone using the “Marker Based Watershed Inside Mask” algorithms available in Amira software. The complete segmentations of the 31 possible bones present in the murine hindpaw are shown from the dorsal (D) and plantar (E) viewpoints, and the volumes of each individual bone were subsequently extracted for downstream analysis.

Article Snippet: Together, these inputs generate bone-specific segmentations that expand to the full volume of the bone using the “Marker Based Watershed Inside Mask” algorithms available in Amira software.

Techniques: Micro-CT, Marker, Software

Journal: Bone Reports

Article Title: A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets

doi: 10.1016/j.bonr.2022.101167

Figure Lengend Snippet: Automated workflow for generation of bone-specific watershed seeds. To create the bone-specific watershed seeds in an efficient and reproducible manner, an automated workflow was developed and packaged as a convenient “Recipe” where all steps are embedded within a single module in Amira. To visualize the changes at each step in the recipe, a 2D section near the tarsal region of a representative hindpaw is shown, while the modules function on the full 3D dataset. The recipe requires the input of the dataset after application of a three-dimensional median filter (A) and a binary representation of the bone (>2500 HU). A top-hat was then applied to identify local valleys in signal intensity, and a threshold >10 HU was set to define the valley depth (B). The top hat segmentation was then subtracted from the binary representation of the bone to define only high-intensity regions with limited signal change in adjacent voxels (C). The result was then eroded to further separate the intense regions of the individual bones (D), and the separate objects were then defined as individual materials with connected voxels sharing at least one common vertex (E). Small objects were then removed to isolate the larger connected materials defining the majority of the individual bones as the final step in the generation of the watershed seeds (F). An automated workflow was used to create the binary mask, which utilizes the same approach as shown in A-C, but with an increased threshold for valley depth at >750 HU in the top-hat step (G). The filtered dataset (A), watershed seeds (bone-specific, color-annotated contours) (F), and binary mask (blue contours) (G) were combined together in the “Marker Based Watershed Inside Mask” Amira module for the complete segmentation of the individual bones in the hindpaw (H), as described in .

Article Snippet: Together, these inputs generate bone-specific segmentations that expand to the full volume of the bone using the “Marker Based Watershed Inside Mask” algorithms available in Amira software.

Techniques: Marker

Journal: Bone Reports

Article Title: A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets

doi: 10.1016/j.bonr.2022.101167

Figure Lengend Snippet: Semi-automated segmentation produces connected or split errors prior to user correction. During the automated watershed seed development, approximately 15% of the seeds will be erroneously connected (A-C), 5% will be incorrectly split (D–F), and 80% will be correctly segmented without user intervention (G,H). After correction of the erroneous watershed seeds shown in B to C and E to F, the watershed seeds can then be used in the “Marker-Based Watershed Inside Mask” module for segmentation of the hindpaw, where the example bones are identified in the final segmentation by stars with corresponding colors (I). Note that in quantification of the error rate, all of the bones connected as in B are considered as individual errors ( i.e. 3 bones connected means 3 errors), while the split bone as in E is considered a single error.

Article Snippet: Together, these inputs generate bone-specific segmentations that expand to the full volume of the bone using the “Marker Based Watershed Inside Mask” algorithms available in Amira software.

Techniques: Marker

Journal: Bone Reports

Article Title: A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets

doi: 10.1016/j.bonr.2022.101167

Figure Lengend Snippet: Methods for correcting errors in the automated watershed seed placement. To demonstrate the approach for correcting the connected and split errors, the datasets shown in are used as examples. When connected errors occur as with the CUB, NAVLAT, and MED shown in 3D (A; red selection from left to right) and 2D (B; red filled in contours from top to bottom), there are 2 primary methods to fix these mistakes. First, the edges of the predominant watershed seeds of a specific bone may already be correctly split, but labeled as the same material because of a connected vertex in 3D. By clicking inside the seeds within a particular bone (C, i.e. MED), the bone may segment independently from the connected bones and can be added as a new and separate material (note red seeds converted to pink seeds) (D). However, if clicking within the bone-specific seeds does not separate the bones (E; CUB and NAVLAT remain connected with the red contours filled in), new seeds can be quickly generated using the magic wand tool. Starting with a mask of 4500 HU, click within the bone of interest and sequentially increase the mask by 250 HU until the bone separates (F; NAVLAT is selected as purple, while CUB is not selected as blue). The NAVLAT can then be added as a new and separate material then the process repeated for CUB (G) to create the new watershed seeds (H). Importantly, the material that represented the original, erroneously connected watershed seeds must be deleted to finalize the corrected watershed seeds as shown in 3D (I), and 2D (J). On the other hand, split errors, as in the example of the 2nd metatarsal (K; MET2), can be fixed by naming the two components of the bone as MET2, which will then be merged (L) to correct the error (M). As a last resort, the watershed seeds can be placed manually by visualizing the 2D crosshairs in the center of the bone to be segmented, and small dots placed around the bone using the brush tool in the XY (N), XZ (O), and YZ (P) planes. The crosshairs can also be visualized in 3D to confirm the identification of the bone being segmented (Q). The manual seed placement is also helpful in situations where the automated watershed seeds do not adequately segment the articulating surfaces of two bones and generate an unclearly segmented border. A detailed description of these correction processes is also provided in the Supplementary Methods and Supplementary Video 3.

Article Snippet: Together, these inputs generate bone-specific segmentations that expand to the full volume of the bone using the “Marker Based Watershed Inside Mask” algorithms available in Amira software.

Techniques: Selection, Labeling, Generated