Abstract: A system and method for generating a fusion of volumetric images and surface scan images said system comprising: a processor configuring the system to: receive both a volumetric image tooth mesh and surface scan image tooth crown mesh from a same patient, registered to a similar coordinate system; segment by anatomical structure each of the registered meshes that are in common between each of the registered volumetric image tooth mesh and the surface scan tooth crown mesh; and recognize a fusion vertices for each of the segmented volumetric image tooth mesh and segmented surface scan tooth crown mesh for matching the recognized meshes; remove a surface fragment from the matched volumetric image mesh in common with the matched surface scan image mesh for removal from the volumetric image mesh; and fuse the meshes by triangulating the recognized fusion vertices.

Abstract: A method for predicting at least one of a tooth crown or implant feature, said method comprising the steps of: receiving at least one of a volumetric or surface scan image, wherein the volumetric image is a three-dimensional voxel array of a maxillofacial anatomy of a patient and the surface scan image is a polygonal mesh of a maxillofacial anatomy of the patient; segmenting at least one of the volumetric image or surface scan image into a set of distinct anatomical structures by assigning each voxel an identifier by structure and assigning at least one of a vertices, face, or points on the mesh an identifier by structure for the volumetric image and surface scan image, respectively, wherein the distinct anatomical structures include at least one of a tooth, jaw, mandibular canal, maxillary sinus, fossae, and a missing tooth; and predicting at least one of a tooth crown or implant feature in place of the segmented missing tooth, wherein the predicted crown and/or implant feature is at least one of generated o

Abstract: A method for alignment of volumetric and surface scan images, said method comprising the steps of: receiving a volumetric image and surface scan image, wherein the volumetric image is a three-dimensional voxel array of a maxillofacial anatomy of a patient and the surface scan image is a polygonal mesh corresponding to the maxillofacial anatomy of the same patient; segmenting the volumetric image and surface scan image into a set of distinct anatomical structures by assigning each voxel in the volumetric image an identifier by structure and assigning each vertex or face of the mesh from the surface scan image an identifier by structure, wherein at least one of the distinct anatomical structures are in common between the volumetric and the surface scan image; extracting a polygonal mesh from the volumetric image featuring common structures with the polygonal mesh from the surface scan image; converting both meshes from the volumetric image and from the surface scan to a point cloud; and aligning the converted m

Abstract: A method for automated segmentation of volumetric images comprising the steps of: receiving a coarse volumetric image comprising a jaw/tooth structure in terms of voxels; defining each voxel a distinct anatomical identifier based on a probabilistic distribution for each of a tooth/non-tooth class; applying the defined coarse image through a coarse model for a coarse output; combining coarse output with the coarse volumetric image; and generating a probabilistic segmentation from applying the combined coarse output/image through a fine model; and converting the probabilistic to a polygonal mesh for each defined class applying a volume-to-mesh algorithm.

Abstract: Disclosed is a system and method for classifying a tooth condition, comprising: a localization layer; a classification layer; a processor; a non-transitory storage element coupled to the processor; encoded instructions stored in the non-transitory storage element, wherein the encoded instructions when implemented by the processor, configure the automated parsing pipeline system to: localize a present anatomical landmark in a patient's oral or maxillofacial region depicted in a parsed image frame by the localization layer, said landmark comprising of a feature or features with anatomical or pathological significance; and classify a dental or maxillofacial condition based on measuring any kind of relation between two or more features within and/or between landmarks by the classification layer.

Abstract: Well settings in mature oil field may be configured by generating progressively, geological reservoir models by fitting a set of initial geological reservoir models that generate forecasts of fluid injection and production at a well as a function of time, with a subset of historical well data to produce a set of intermediary geological reservoir models, and fitting the set of intermediary geological reservoir models to a next subset of the historical well data. A set of diverse geological reservoir models may be determined from the geological reservoir models. A mid-term schedule of well settings associated with the well may be generated based on the set of diverse geological reservoir models and an economic model that comprises mid-term estimation of oil sale price and of production costs. The mid-term schedule of well settings may be actuated to control the fluid injection and production at the well.

Abstract: Configuring well settings may include determining schedules over a mid-term horizon for well settings for all or a subset of wells in an oil field. The schedule may be generated so that a performance metric for the considered horizon is maximized. An economic metric, for example, a net present value, and production metrics, for example, cumulative oil production, are considered. Minimum acceptable short-term and long-term metrics, for example, short-term and long-term cumulative field oil production, may be included as target/constraints.

Abstract: Well settings in mature oil field may be configured by generating progressively, a plurality of geological reservoir models by fitting a set of initial geological reservoir models that generate forecasts of fluid injection and production at a well as a function of time, with a subset of historical well data to produce a set of intermediary geological reservoir models, and fitting the set of intermediary geological reservoir models to a next subset of the historical well data. A set of diverse geological reservoir models may be determined from the plurality of geological reservoir models. A mid-term schedule of well settings associated with the well may be generated based on the set of diverse geological reservoir models and an economic model that comprises mid-term estimation of oil sale price and of production costs. The mid-term schedule of well settings may be actuated to control the fluid injection and production at the well.

Abstract: Configuring well settings may include determining schedules over a mid-term horizon for well settings for all or a subset of wells in an oil field. The schedule may be generated so that a performance metric for the considered horizon is maximized. An economic metric, for example, a net present value, and production metrics, for example, cumulative oil production, are considered. Minimum acceptable short-term and long-term metrics, for example, short-term and long-term cumulative field oil production, may be included as target/constraints.