Predicting total nucleic acid yield and dissection boundaries for histology slides

1. A computer-implemented method comprising:

receiving a first hematoxylin and eosin (H&E) slide prepared from a tumor block at an image-based nucleic acid yield prediction system having one or more processors;

identifying, using the one or more processors, tumor cells of the H&E slide from a digital image;

generating a tumor area mask based at least in part on the identified tumor cells, wherein the tumor area mask defines a dissection boundary of the H&E slide associated with the digital image;

predicting an expected yield of nucleic acid for the tumor cells within the dissection boundary, by providing the digital image to a machine learning model trained on a plurality of H&E slides having labeled dissection boundaries and labeled total nucleic yield;

based on the predicted expected yield of nucleic acid, identifying a number of H&E slides to prepare from the tumor block to satisfy a total nucleic yield; and

preparing the number of H&E slides from the tumor block when the predicted expected yield of nucleic acid exceeds a predetermined threshold.

2. The method of claim 1 , wherein the predetermined threshold is based at least in part on one or more specification requirements.

3. The method of claim 2 , wherein specification requirements are based on a minimum surface area within the dissection boundary.

4. The method of claim 2 , wherein specification requirements are based on a minimum cell count within the dissection boundary.

5. The method of claim 2 , wherein specification requirements are based on a minimum cell density within the dissection boundary.

6. The method of claim 2 , wherein specification requirements are set by an entity.

7. The method of claim 1 , wherein the predetermined threshold is selected from a range between and including 50 ng-2000 ng.

8. The method of claim 1 , wherein predicting the expected yield of nucleic acid for the tumor cells within the dissection boundary comprises:

counting the number of tumor cells identified within the dissection boundary and multiplying the count by a tumor cell average nucleic acid yield.

9. The method of claim 1 , wherein predicting the expected yield of nucleic acid for the tumor cells within the dissection boundary comprises:

calculating the surface area of the dissection boundary and multiplying the surface area by a dissection boundary average nucleic acid yield.

10. The method of claim 1 , wherein the dissection boundary is a microdissection boundary.

11. The method of claim 1 , wherein the dissection boundary is a macrodissection boundary.

12. The method of claim 1 , wherein the dissection boundary is a whole slide boundary.

13. The method of claim 1 , wherein identifying the tumor cells of the H&E slide from the digital image comprises identifying the tumor cells using a trained cell segmentation model.

14. The method of claim 13 , wherein identifying the tumor using the trained cell segmentation model comprises:

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

15. The method of claim 14 , wherein assigning the cell classification to one or more pixels within the tile image comprises:

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.

16. The method of claim 13 , wherein the trained cell segmentation model is a pixel-resolution three-dimensional UNet classification model trained to classify a cell interior, a cell border, and a cell exterior.

17. The method of claim 13 , wherein identifying the tumor cells using the trained cell segmentation model comprises:

applying, using the one or more processors, each of a plurality of tile images formed from the digital image to the trained cell segmentation model; and

performing, using the one or more processors, a registration on segmented cells in each of the tile images by determining a cell border of each cell, determining a centroid of each cell, and shifting coordinates of centroids to a universal coordinate space for the digital image.

18. The method of claim 13 , wherein the trained cell segmentation model is trained using a set of H&E slide training images annotated with identified cell borders, identified cell interiors, and identified cell exteriors.

19. The method of claim 1 , further comprising superimposing, using a viewer, the tumor area mask over the digital image to visually indicate to a user which tumor cells to scrape.

20. The method of claim 1 , wherein generating the tumor area mask further comprises providing the digital image of the H&E slide to a model trained on a plurality of H&E slides having dissection labels.

21. A system comprising a pathology slide scanner system configured to perform the method of claim 1 .

22. The system of claim 21 , further configured to generate a digital image of each of the one or more H&E slides from the tumor block.

23. The system of claim 22 , wherein the pathology slide scanner system is communicatively coupled to an image-based tumor cells prediction system through a communication network for providing the one or more of digital images.

24. The method of claim 1 , wherein the one or more processors are one or more graphics processing units (GPUs), tensor processing units (TPUs), and/or central processing units (CPUs).

25. The method of claim 1 , wherein the expected yield of nucleic acid is confirmed through sequencing the tumor cells within the dissection boundary.

26. The method of claim 25 , where sequencing is next-generation sequencing.

27. The method of claim 25 , where sequencing is short-read sequencing.