Journal: Biomedical Optics Express

Article Title: Automated robot-assisted wide-field optical coherence tomography using structured light camera

doi: 10.1364/BOE.496710

Figure Lengend Snippet: Schematic diagram of image segmentation and 3D reconstruction based on the BiSeNetV2 network. (a) The OCT probe moves uniformly along the predetermined path, capturing consecutive OCT B-scans. (b) Architecture of the BiSeNetV2 model. (c) 3D visualization after background removal through segmentation. (d) 3D visualization prior to segmentation. (e) OCT B-scan image. (f) Segmentation prediction result.

Article Snippet: The 3D structured light camera (RVC-I2370, RVBUST, Inc.) utilizes 465 nm blue stripe structured light and an advanced point cloud synthesis algorithm to capture images of a surface.

Techniques:

Journal: Biomedical Optics Express

Article Title: Automated robot-assisted wide-field optical coherence tomography using structured light camera

doi: 10.1364/BOE.496710

Figure Lengend Snippet: Experimental results of scanning skin phantom. (a) Photograph of the skin phantom with black paper strips pasted on it as a size reference. (b) 3D OCT visualization. (c) Point cloud obtained by structured light camera (blue) and OCT surface point cloud (sparse sampling). (d) Stitched en face OCT image of the phantom surface. (e) Depth-encoded map of the scanned region. (f) and (g) OCT cross-sections at the yellow and red lines in (b), respectively.

Article Snippet: The 3D structured light camera (RVC-I2370, RVBUST, Inc.) utilizes 465 nm blue stripe structured light and an advanced point cloud synthesis algorithm to capture images of a surface.

Techniques: Sampling

Journal: Biomedical Optics Express

Article Title: Automated robot-assisted wide-field optical coherence tomography using structured light camera

doi: 10.1364/BOE.496710

Figure Lengend Snippet: Results of large area imaging on the isolated kidney. (a) Photographs depicting the isolated kidney and the selection of the region of interest (ROI). (b) 3D OCT visualization. (c) Color point cloud captured by the structured light camera. (d) Stitched en face OCT image displaying the surface of the kidney. (e) Depth-encoded map representing the scanned region. (f) B-scan and semantic segmentation results. (g) and (h) OCT cross-sections captured at the yellow and red lines shown in (b), respectively.

Article Snippet: The 3D structured light camera (RVC-I2370, RVBUST, Inc.) utilizes 465 nm blue stripe structured light and an advanced point cloud synthesis algorithm to capture images of a surface.

Techniques: Imaging, Isolation, Selection

Journal: Biomedical Optics Express

Article Title: Automated robot-assisted wide-field optical coherence tomography using structured light camera

doi: 10.1364/BOE.496710

Figure Lengend Snippet: Large area imaging results of the skin on the back of the hand with scars. (a) The hand was immobilized to minimize tremors during the imaging process. (b) 3D visualization of the hand's skin using OCT imaging. (c) Color point cloud captured by the structured light camera. (d) Stitched en face image displaying the surface of the skin, with clearly distinguishable scars indicated by red arrows. (e) Depth-encoded map representing the scanned area. (f) Cross-section of scar tissue. (g), (h) OCT cross-sections captured at the yellow and red lines shown in (b), respectively. High resolution image of (h) shown in Dataset 1 (Ref. [43]).

Article Snippet: The 3D structured light camera (RVC-I2370, RVBUST, Inc.) utilizes 465 nm blue stripe structured light and an advanced point cloud synthesis algorithm to capture images of a surface.

Techniques: Imaging

Journal: Biomedical Optics Express

Article Title: Automated robot-assisted wide-field optical coherence tomography using structured light camera

doi: 10.1364/BOE.496710

Figure Lengend Snippet: Large area imaging results of the leaf blade. (a) Photograph of the leaf blade highlighting the scanned area. (b) 3D visualization. (c) Color point cloud captured by the structured light camera. (d) Stitched en face image showcasing the surface of the leaf, with distinct visualization of leaf edges and veins. (e) Depth-encoded map representing the scanned area of the leaf. (f) The B-scan image provides a cross-sectional view of the blade structure. (g) and (h) OCT cross-sections captured at the yellow and red lines indicated in (b), respectively. High resolution image of (h) shown in Dataset 1 (Ref. [43]).

Article Snippet: The 3D structured light camera (RVC-I2370, RVBUST, Inc.) utilizes 465 nm blue stripe structured light and an advanced point cloud synthesis algorithm to capture images of a surface.

Techniques: Imaging

Journal: Biomedical Optics Express

Article Title: Automated robot-assisted wide-field optical coherence tomography using structured light camera

doi: 10.1364/BOE.496710

Figure Lengend Snippet: Large area imaging results of citrus. (a) Photograph depicting the citrus sample and the corresponding scanned area. (b) 3D visualization. (c) Color point cloud captured by the structured light camera. (d) Stitched en face image showcasing the surface of the citrus, with clear visualization and distinction of the scars. (e) Depth-encoded map representing the scanned area of the citrus. (f) B-scan images depicting cross-sections of scar tissue. (g) and (h) OCT cross-sections captured at the yellow and red lines indicated in (b), respectively. High resolution image of (h) shown in Dataset 1 (Ref. [43]).

Article Snippet: The 3D structured light camera (RVC-I2370, RVBUST, Inc.) utilizes 465 nm blue stripe structured light and an advanced point cloud synthesis algorithm to capture images of a surface.

Techniques: Imaging

Journal: Biomedical Optics Express

Article Title: Automated robot-assisted wide-field optical coherence tomography using structured light camera

doi: 10.1364/BOE.496710

Figure Lengend Snippet: Large area imaging results of ceramics. (a) Photograph illustrating the ceramic sample and the corresponding scanned area. (b) 3D visualization. (c) Color point cloud captured by the structured light camera. (d) Stitched en face image of the ceramic surface, showcasing areas of high surface reflection and brightness. (e) Depth-coded map representing the scanned area of the ceramics, facilitating depth visualization. (f) Semantic segmentation results with the background portion highlighted in green, the enamel portion highlighted in yellow, and the base highlighted in red. (g) and (h) OCT cross-sections captured at the yellow and red lines indicated in (b), respectively, allowing detailed analysis at specific locations within the ceramics. High resolution image of (h) shown in Dataset 1 (Ref. [43]).

Article Snippet: The 3D structured light camera (RVC-I2370, RVBUST, Inc.) utilizes 465 nm blue stripe structured light and an advanced point cloud synthesis algorithm to capture images of a surface.

Techniques: Imaging

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: Infants face were scanned with an infrared structured light camera (Bellus 3D™) which projected 50,000 infrared dots onto the face area. Using proprietary (Bellus 3D™) landmark recognition software surface images are created with close range scanning at up tp 0.4 mm resolution

Article Snippet: We determined the accuracy of the facial contour of the human head model obtained with the structured light scanning (Bellus 3D Camera) when compared with the contour obtained by computed tomography (CT) of a facial model to be highly accurate (< 1 mm, Fig. ).

Techniques: Software

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: a ) A point cloud created as 500,000 3D surface points recognized by structured infrared light scanning software. b ) High density surface face model created with 250,00 high-definition triangles. c ) Texture map applied to the surface of high-definition triangles ( b ) which can be used for direct 3D printing

Article Snippet: We determined the accuracy of the facial contour of the human head model obtained with the structured light scanning (Bellus 3D Camera) when compared with the contour obtained by computed tomography (CT) of a facial model to be highly accurate (< 1 mm, Fig. ).

Techniques: Software

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: Part comparison analysis of measurements determined by structed light scanning camera (Bellus) compared to CT scanned surface map. a ) The structured light scanning 3D data was superimposed onto the CT scanned image surface and the comparison to the 3D model was assessed by color mapping. b ) Color scale representing the difference in mm between each point of the 3D surface with the corresponding CT surface data. Green = 0.0 mm difference. Red = 2.0 mm difference. c ) A histogram depicting the number of points that correspond to each color

Article Snippet: We determined the accuracy of the facial contour of the human head model obtained with the structured light scanning (Bellus 3D Camera) when compared with the contour obtained by computed tomography (CT) of a facial model to be highly accurate (< 1 mm, Fig. ).

Techniques: Comparison

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: 9 representative structured facial scan of neonates using Bellus 3D camera and Bellus 3D scanning software

Article Snippet: We determined the accuracy of the facial contour of the human head model obtained with the structured light scanning (Bellus 3D Camera) when compared with the contour obtained by computed tomography (CT) of a facial model to be highly accurate (< 1 mm, Fig. ).

Techniques: Software

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: Using structured light scans of neonatal faces individual CPAP inserts were designed to uniquely conform, to facial topography

Article Snippet: We determined the accuracy of the facial contour of the human head model obtained with the structured light scanning (Bellus 3D Camera) when compared with the contour obtained by computed tomography (CT) of a facial model to be highly accurate (< 1 mm, Fig. ).

Techniques:

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: Infants face were scanned with an infrared structured light camera (Bellus 3D™) which projected 50,000 infrared dots onto the face area. Using proprietary (Bellus 3D™) landmark recognition software surface images are created with close range scanning at up tp 0.4 mm resolution

Article Snippet: Fig. 1 Infants face were scanned with an infrared structured light camera (Bellus 3DTM) which projected 50,000 infrared dots onto the face area.

Techniques: Software

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: a ) A point cloud created as 500,000 3D surface points recognized by structured infrared light scanning software. b ) High density surface face model created with 250,00 high-definition triangles. c ) Texture map applied to the surface of high-definition triangles ( b ) which can be used for direct 3D printing

Article Snippet: Fig. 1 Infants face were scanned with an infrared structured light camera (Bellus 3DTM) which projected 50,000 infrared dots onto the face area.

Techniques: Software

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: Part comparison analysis of measurements determined by structed light scanning camera (Bellus) compared to CT scanned surface map. a ) The structured light scanning 3D data was superimposed onto the CT scanned image surface and the comparison to the 3D model was assessed by color mapping. b ) Color scale representing the difference in mm between each point of the 3D surface with the corresponding CT surface data. Green = 0.0 mm difference. Red = 2.0 mm difference. c ) A histogram depicting the number of points that correspond to each color

Article Snippet: Fig. 1 Infants face were scanned with an infrared structured light camera (Bellus 3DTM) which projected 50,000 infrared dots onto the face area.

Techniques: Comparison

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: 9 representative structured facial scan of neonates using Bellus 3D camera and Bellus 3D scanning software

Article Snippet: Fig. 1 Infants face were scanned with an infrared structured light camera (Bellus 3DTM) which projected 50,000 infrared dots onto the face area.

Techniques: Software

Journal: 3D Printing in Medicine

Article Title: Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning

doi: 10.1186/s41205-022-00155-7

Figure Lengend Snippet: Using structured light scans of neonatal faces individual CPAP inserts were designed to uniquely conform, to facial topography

Article Snippet: Fig. 1 Infants face were scanned with an infrared structured light camera (Bellus 3DTM) which projected 50,000 infrared dots onto the face area.

Techniques: