Quantitatively Characterizing Disease Morphology With Co-Occurring Gland Tensors In Localized Subgraphs
First Claim
1. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a computer cause the computer to perform a method of associating gland orientation disorder with malignancy and risk of post-surgical biochemical recurrence (BCR) in a prostate cancer (CaP) patient, the method comprising:
- accessing a digitized image of a section of a prostate demonstrating pathology associated with CaP in the patient;
detecting a gland in a region of interest of the digitized image;
segmenting an individual gland boundary in the region of interest in the digitized image into a set of gland boundary points;
producing a gland tensor by associating a tensor with the gland, where the gland tensor indicates the dominant orientation of the gland, and where the gland tensor is based on the major axis of the gland;
constructing a subgraph of a localized gland network within the region of interest, where constructing the subgraph comprises linking individual glands located proximal to each other into the localized gland network, where the nodes of the subgraph represent individual gland centroids, and where the edges of the subgraph are defined between pairs of glands by a probabilistic decaying function;
constructing a tensor co-occurrence matrix, where elements of the tensor co-occurrence matrix comprise gland tensor pairs, where the gland tensor pairs are defined by the subgraph, and where the tensor co-occurrence matrix aggregates co-occurring gland tensors based, at least in part, on the frequency with which orientations of two individual glands located proximal to each other co-occur;
deriving second-order statistics of gland orientations in the localized gland networks in the digitized image;
selectively differentiating a cancerous tissue region from a non-cancerous tissue region in the image based, at least in part, on the second-order statistics; and
establishing a BCR score for the patient based, at least in part, on the second-order statistics.
1 Assignment
0 Petitions
Accused Products
Abstract
Apparatus, methods, and other embodiments associated with objectively predicting biochemical recurrence with co-occurring gland tensors in localized subgraphs are described. One example apparatus includes a set of logics that associate directional disorder with a risk of failure in a material. A first logic detects a fundamental unit of composition in the material, segments boundaries of the fundamental unit, and calculates a directional tensor for the fundamental unit. A second logic constructs a localized sparsified subgraph whose nodes represent centroids of the fundamental units, defines pairwise spatial relationships between the fundamental units, and constructs a directional co-occurrence matrix based on the spatial relationships. A third logic derives second order statistical features from the co-occurrence matrix, and produces a risk failure score as a function of the second order statistical features. The second order statistical features include the entropy of the directional organization of the fundamental units.
-
Citations
24 Claims
-
1. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a computer cause the computer to perform a method of associating gland orientation disorder with malignancy and risk of post-surgical biochemical recurrence (BCR) in a prostate cancer (CaP) patient, the method comprising:
-
accessing a digitized image of a section of a prostate demonstrating pathology associated with CaP in the patient; detecting a gland in a region of interest of the digitized image; segmenting an individual gland boundary in the region of interest in the digitized image into a set of gland boundary points; producing a gland tensor by associating a tensor with the gland, where the gland tensor indicates the dominant orientation of the gland, and where the gland tensor is based on the major axis of the gland; constructing a subgraph of a localized gland network within the region of interest, where constructing the subgraph comprises linking individual glands located proximal to each other into the localized gland network, where the nodes of the subgraph represent individual gland centroids, and where the edges of the subgraph are defined between pairs of glands by a probabilistic decaying function; constructing a tensor co-occurrence matrix, where elements of the tensor co-occurrence matrix comprise gland tensor pairs, where the gland tensor pairs are defined by the subgraph, and where the tensor co-occurrence matrix aggregates co-occurring gland tensors based, at least in part, on the frequency with which orientations of two individual glands located proximal to each other co-occur; deriving second-order statistics of gland orientations in the localized gland networks in the digitized image; selectively differentiating a cancerous tissue region from a non-cancerous tissue region in the image based, at least in part, on the second-order statistics; and establishing a BCR score for the patient based, at least in part, on the second-order statistics. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
-
-
14. A method for controlling a computer to compute a biochemical recurrence (BCR) score for a cancer patient, comprising:
-
detecting a gland in a region of interest of a digitized histopathology section associated with the cancer patient; segmenting gland boundaries of the gland into a set of gland boundaries; calculating a directional gland tensor for the gland; constructing a localized subgraph of a gland network in the region of interest; defining pairwise spatial relationships between two or more glands in the subgraph; constructing a directional gland tensor co-occurrence matrix based, at least in part, on the pairwise spatial relationships; deriving second order statistical features from the directional gland tensor co-occurrence matrix; and controlling the computer to produce a BCR recurrence score based, at least in part, on the second order statistical features, where the BCR recurrence score predicts BCR with at least 66% accuracy. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23)
-
-
24. An apparatus, comprising:
-
a processor; a memory; an input/output interface; a set of logics that associate directional disorder with a risk of failure in a region of interest in a material, and an interface to connect the processor, the memory, the input/output interface and the set of logics, the set of logics comprising; a first logic that detects a fundamental unit of composition in the region of interest, segments boundaries of the fundamental unit, and calculates a tensor for the fundamental unit, where the tensor indicates the dominant orientation of the fundamental unit; a second logic that constructs a sparsified localized subgraph from the detected fundamental units, defines pairwise spatial relationships between the fundamental units in the subgraph, and constructs a directional tensor co-occurrence matrix based, at least in part on the pairwise spatial relationships, where the nodes of the sparsified localized subgraph represent the centroids of the fundamental unit; and a third logic that derives second order statistical features from the directional tensor co-occurrence matrix, and produces a risk of failure score based, at least in part, on the second order statistical features.
-
Specification