PREDICTING TOTAL NUCLEIC ACID YIELD AND DISSECTION BOUNDARIES FOR HISTOLOGY SLIDES

1 - 20 . (canceled)

21 . A computer-implemented method, comprising:

receiving, via one or more processors, a digital image of an histology slide;

detecting, via one or more processors, an excess tissue on the slide;

labeling, via one or more processors, the slide as having excess tissue; and

generating, via one or more processors, a notification report indicating the excess tissue.

22 . The computer-implemented method of claim 21 , further comprising:

collecting respective nucleic acid yield of a plurality of cells corresponding to the slide, and

wherein detecting the excess tissue on the slide includes detecting excess tissue of the plurality of cells.

23 . The computer-implemented method of claim 21 , further comprising:

processing imaging features including tumor shape features, cell shape features, and/or cell texture features.

24 . The computer-implemented method of claim 23 , wherein the imaging features include at least one of tumor shape features of tumor area, tumor perimeter, tumor circularity, tumor density, or number of tumors.

25 . The computer-implemented method of claim 23 , wherein the imaging features includes cell shape features of cell area, cell perimeter, cell circularity, and/or cell density.

26 . The computer-implemented method of claim 23 , wherein the imaging features includes cell texture features of RGB texture patterns, grayscale texture patterns, gradient and/or features.

27 . The computer-implemented method of claim 21 , wherein when a predicted expected yield of nucleic acid fails to satisfy a target total nucleic acid yield:

identifying a number of associated unstained slides that satisfies the target total nucleic acid yield; and

accepting the number of associated unstained slides for next-generation sequencing.

28 . The computer-implemented method of claim 27 , wherein the target total nucleic acid yield is selected from a range between and including 50 ng-2000 ng.

29 . The computer-implemented method of claim 21 , wherein associated unstained slides are flagged for scraping.

30 . The computer-implemented method of claim 21 , wherein associated unstained slides include tissue from a formalin-fixed paraffin embedded specimen.

31 . The computer-implemented method of claim 21 , further comprising:

applying, via one or more processors, a plurality of tile images formed from the digital image to a trained cell segmentation model and, for each tile, assigning a cell classification to one or more pixels within the tile image.

32 . The computer-implemented method of claim 31 , further comprising:

identifying, using the one or more processors, the one or more pixels as a cell interior, a cell border, or a cell exterior and classifying the one or more pixels as the cell interior, the cell border, or the cell exterior.

33 . The computer-implemented method of claim 21 , further comprising:

receiving, at an image-based nucleic acid yield prediction system having one or more processors, digital images of the H&E slides prepared from a tumor block;

identifying, via the one or more processors, tumor cells within each digital image;

predicting, for each digital image, an expected yield of nucleic acid for the tumor cells;

determining, based on the predicted expected yield of nucleic acid and a predetermined threshold, a quality control (QC) status for each H&E slide;

flagging a respective QC status in one or more of the H&E slides when the predicted expected yield of nucleic acid or the amount of tumor tissue exceeds a predetermined threshold, indicating potential for more efficient use of the slide for diagnostic or research purposes; and

generating a report indicating the QC status for each H&E slide, the report including recommendations for further processing based on the QC status,

wherein slides flagged for manual review are presented with overlays indicating the tumor area mask and a rationale for the manual review status.

34 . A computing system, comprising:

one or more processors; and

one or more memories, having stored thereon computer-executable instructions that, when executed, cause the computing system to:

receive, via the one or more processors, a digital image of an histology slide;

detect, via the one or more processors, an excess tissue on the slide;

label, via the one or more processors, the slide as having excess tissue; and

generate, via the one or more processors, a notification report indicating the excess tissue.

35 . The computing system of claim 34 , the memories having stored thereon instructions that, when executed, cause the computing system to:

collect respective nucleic acid yield of a plurality of cells corresponding to the slide, and

detect excess tissue of the plurality of cells.

36 . The computing system of claim 34 , the memories having stored thereon instructions that, when executed, cause the computing system to:

process imaging features including tumor shape features, cell shape features, and/or cell texture features.

37 . The computing system of claim 36 , wherein the imaging features include at least one of tumor shape features of tumor area, tumor perimeter, tumor circularity, tumor density, or number of tumors.

38 . A computer-readable medium having stored thereon computer-executable instructions that, when executed, cause a computer to:

receive, via one or more processors, a digital image of an histology slide;

detect, via one or more processors, an excess tissue on the slide;

label, via one or more processors, the slide as having excess tissue; and

generate, via one or more processors, a notification report indicating the excess tissue.

39 . The computer-readable medium of claim 38 , having stored thereon instructions that, when executed, cause a computer to:

collect respective nucleic acid yield of a plurality of cells corresponding to the slide, and

detect excess tissue of the plurality of cells.

40 . The computer-readable medium of claim 38 , having stored thereon instructions that, when executed, cause a computer to:

process imaging features including tumor shape features, cell shape features, and/or cell texture features.