Abstract: An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Abstract: An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Abstract: An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Abstract: An apparatus, system and process for utilizing dual complementary pattern illumination of a scene when performing depth reconstruction of the scene are described. The method may include projecting a first reference image and a complementary second reference image on a scene, and capturing first image data and second image data including the first reference image and the complementary second reference image on the scene. The method may also include identifying features of the first reference image from features of the complementary second reference image. Furthermore, the method may include performing three-dimensional (3D) scene reconstruction for image data captured by the imaging device based on the identified features in the first reference image.

Abstract: A system for medical diagnosis includes a fiber optic cable and a plurality of light emitters optically coupled to a first end of the fiber optic cable. Each light emitter in the plurality of light emitters emits a distinct bandwidth of light. The system also includes a controller electrically coupled to the plurality of light emitters. The controller includes logic that when executed by the controller causes the controller to perform operations including: receiving instructions including an illumination mode, and adjusting an intensity of the light emitted from each light emitter in the plurality of light emitters to match the illumination mode.

Abstract: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.

Abstract: An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Abstract: A system for medical diagnosis includes a fiber optic cable and a plurality of light emitters optically coupled to a first end of the fiber optic cable. Each light emitter in the plurality of light emitters emits a distinct bandwidth of light. The system also includes a controller electrically coupled to the plurality of light emitters. The controller includes logic that when executed by the controller causes the controller to perform operations including: receiving instructions including an illumination mode, and adjusting an intensity of the light emitted from each light emitter in the plurality of light emitters to match the illumination mode.

Abstract: An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Abstract: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.

Abstract: An apparatus, system and process for utilizing dual complementary pattern illumination of a scene when performing depth reconstruction of the scene are described. The method may include projecting a first reference image and a complementary second reference image on a scene, and capturing first image data and second image data including the first reference image and the complementary second reference image on the scene. The method may also include identifying features of the first reference image from features of the complementary second reference image. Furthermore, the method may include performing three-dimensional (3D) scene reconstruction for image data captured by the imaging device based on the identified features in the first reference image.

Abstract: An endoscope apparatus includes a fiber optic cable with a proximal end and a distal end opposite the proximal end. The endoscope apparatus also includes a light source optically coupled to the proximal end of the fiber optic cable to emit visible light and excitation light into the fiber optic cable for output from the distal end. The light source is configured to emit both the visible light and the excitation light simultaneously, and a wavelength of the excitation light is outside a wavelength spectrum of the visible light. An image sensor is configured to receive a reflection of the visible light as reflected visible light.

Abstract: An imaging system (e.g., hyperspectral imaging system) receives an indication to compare a first object and a second object (e.g., two anatomical structures or organs in a medical environment). The imaging system accesses a classification vector for the first object and the second object, the classification vector having been extracted by separating a plurality of collected reflectance values for the first object from a plurality of collected reflectance values for the second object. A set of optimal illumination intensities for one or more spectral illumination sources of the imaging system is determined based on the extracted classification vector. The first and second objects are illuminated with the determined illumination intensities. A high-contrast image of the first and second objects is provided for display, such that the two objects can be readily distinguished in the image. The intensity of pixels in the image is determined by the illumination intensities.

Abstract: An apparatus, system and process for utilizing dual complementary pattern illumination of a scene when performing depth reconstruction of the scene are described. The method may include projecting a first reference image and a complementary second reference image on a scene, and capturing first image data and second image data including the first reference image and the complementary second reference image on the scene. The method may also include identifying features of the first reference image from features of the complementary second reference image. Furthermore, the method may include performing three dimensional (3D) scene reconstruction for image data captured by the imaging device based on the identified features in the first reference image.

Abstract: An endoscope apparatus includes a fiber optic cable with a proximal end and a distal end opposite the proximal end. The endoscope apparatus also includes a light source optically coupled to the proximal end of the fiber optic cable to emit visible light and excitation light into the fiber optic cable for output from the distal end. The light source is configured to emit both the visible light and the excitation light simultaneously, and a wavelength of the excitation light is outside a wavelength spectrum of the visible light. An image sensor coupled to the distal end of the fiber optic cable and positioned to receive a reflection of the visible light as reflected visible light.

Abstract: An endoscope apparatus includes a fiber optic cable with a proximal end and a distal end opposite the proximal end. The endoscope apparatus also includes a light source optically coupled to the proximal end of the fiber optic cable to emit visible light and excitation light into the fiber optic cable for output from the distal end. The light source is configured to emit both the visible light and the excitation light simultaneously, and a wavelength of the excitation light is outside a wavelength spectrum of the visible light. An image sensor is configured to receive a reflection of the visible light as reflected visible light.

Abstract: An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Abstract: An endoscope apparatus includes a fiber optic cable with a proximal end and a distal end opposite the proximal end. The endoscope apparatus also includes a light source optically coupled to the proximal end of the fiber optic cable to emit visible light and excitation light into the fiber optic cable for output from the distal end. The light source is configured to emit both the visible light and the excitation light simultaneously, and a wavelength of the excitation light is outside a wavelength spectrum of the visible light. An image sensor coupled to the distal end of the fiber optic cable and positioned to receive a reflection of the visible light as reflected visible light.

Abstract: A system for medical diagnosis includes a fiber optic cable and a plurality of light emitters optically coupled to a first end of the fiber optic cable. Each light emitter in the plurality of light emitters emits a distinct bandwidth of light. The system also includes a controller electrically coupled to the plurality of light emitters. The controller includes logic that when executed by the controller causes the controller to perform operations including: receiving instructions including an illumination mode, and adjusting an intensity of the light emitted from each light emitter in the plurality of light emitters to match the illumination mode.

Abstract: An apparatus, system and process for utilizing dual complementary pattern illumination of a scene when performing depth reconstruction of the scene are described. The method may include projecting a first reference image and a complementary second reference image on a scene, and capturing first image data and second image data including the first reference image and the complementary second reference image on the scene. The method may also include identifying features of the first reference image from features of the complementary second reference image. Furthermore, the method may include performing three dimensional (3D) scene reconstruction for image data captured by the imaging device based on the identified features in the first reference image.