APPARATUS AND METHOD FOR IDENTIFYING REGIONS OF INTEREST DURING SLIDE DIGITIZATION

20250104847

18/972909

December 07, 2024

An apparatus for identifying regions of interest during slide digitization is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a user dataset associated with at least a pathology slide. The memory contains instructions configuring the processor to identify one or more regions of interest within at least a pathology slide as a function of the user dataset. The memory contains instructions configuring the processor to identify at least one scan parameter as a function of the one or more regions of interest. The memory contains instructions configuring the processor to generate a digitized slide by scanning the at least a pathology slide as a function the at least one scan parameter.

Pramana, Inc. (Cambridge, MA, US)

1. An apparatus for identifying regions of interest during slide digitization, wherein the apparatus comprises: an image capture device, wherein the image capture device comprises an image sensor and is configured to scan pathology slides; at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: generate, using the image capture device, an initial scan of at least a pathology slide; receive a user dataset associated with the at least a pathology slide; identify one or more regions of interest within the at least a pathology slide as a function of the user dataset, wherein identifying the one or more regions of interest within the at least a pathology slide comprises: training an identification machine-learning model using identification training data, wherein the identification training data comprises exemplary user datasets correlated to exemplary regions of interest; and generating, using the identification machine-learning model, the one or more regions of interest as a function of the user dataset; determine at least a scan parameter, wherein determining the at least a scan parameter comprises identifying a first scan parameter as a function of the one or more regions of interest; adjust at least a device parameter of the image capture device as a function of the at least a scan parameter and the one or more regions of interest; and generate a digitized slide by scanning the at least a pathology slide using the image capture device comprising the adjusted at least a device parameter.

2. The apparatus of claim 1, wherein receiving the user dataset comprises generating textual data as a function of the initial scan using optical character recognition.

3. The apparatus of claim 1, wherein determining the at least a scan parameter comprises: training a machine-learning model with parameter training data, wherein the parameter training data correlates a plurality of regions of interest to a plurality of scan parameters; and identifying the scan parameter as a function of the one or more regions of interest using the trained machine-learning model.

4. The apparatus of claim 3, wherein determining the at least a scan parameter comprises: calculating an accuracy score for the machine-learning model, wherein the accuracy score indicates a degree of retraining needed for the machine-learning model; retraining the machine-learning model as a function of the accuracy score; and identifying a second scan parameter using the retrained machine-learning model.

5. The apparatus of claim 1, wherein identifying the one or more regions of interest comprises identifying the one or more regions of interest using an image processing module.

6. The apparatus of claim 1, wherein identifying the one or more regions of interest comprises isolating the one or more regions of interest by segmenting a region into a plurality of sub-regions as a function of the one or more regions of interest.

7. The apparatus of claim 1, wherein the at least a scan parameter comprises a magnification level.

8. The apparatus of claim 1, wherein the at least a scan parameter comprises a focus depth.

9. The apparatus of claim 1, wherein the at least a scan parameter comprises a resolution level.

10. The apparatus of claim 1, wherein displaying the digitized slide comprises: receiving a user interaction associated with the one or more regions of interest of the displayed digitized slide; generating slide data as a function of the user interaction; and displaying the slide data attached to the digitized slide.

11. A method for identifying regions of interest during slide digitization, wherein the method comprises: generating, using an image capture device, an initial scan of at least a pathology slide; receiving, using at least a processor, a user dataset associated with the at least a pathology slide; identifying, using the at least a processor, one or more regions of interest within the at least a pathology slide as a function of the user dataset, wherein identifying the one or more regions of interest within the at least a pathology slide comprises: training an identification machine-learning model using identification training data, wherein the identification training data comprises exemplary user datasets correlated to exemplary regions of interest; and generating, using the identification machine-learning model, the one or more regions of interest as a function of the user dataset; determining, using the at least a processor, at least a scan parameter, wherein determining the at least a scan parameter comprises identifying a first scan parameter as a function of the one or more regions of interest; adjusting, using the at least a processor, at least a device parameter of the image capture device as a function of the at least a scan parameter and the one or more regions of interest; and generating, using the at least a processor, a digitized slide by scanning the at least a pathology slide using the image capture device comprising the adjusted at least a device parameter.

12. The method of claim 11, wherein receiving the user dataset comprises generating textual data as a function of the initial scan using optical character recognition.

13. The method of claim 11, wherein determining the at least a scan parameter comprises: training a machine-learning model with parameter training data, wherein the parameter training data correlates a plurality of regions of interest to a plurality of scan parameters; and identifying the scan parameter as a function of the one or more regions of interest using the trained machine-learning model.

14. The method of claim 13, wherein determining the at least a scan parameter comprises: calculating an accuracy score for the machine-learning model, wherein the accuracy score indicates a degree of retraining needed for the machine-learning model; retraining the machine-learning model as a function of the accuracy score; and identifying a second scan parameter using the retrained machine-learning model.

15. The method of claim 11, wherein identifying the one or more regions of interest comprises identifying the one or more regions of interest using an image processing module.

16. The method of claim 11, wherein identifying the one or more regions of interest comprises isolating the one or more regions of interest by segmenting a region into a plurality of sub-regions as a function of the one or more regions of interest.

17. The method of claim 11, wherein the at least a scan parameter comprises a magnification level.

18. The method of claim 11, wherein the at least a scan parameter comprises a focus depth.

19. The method of claim 11, wherein the at least a scan parameter comprises a resolution level.

20. The method of claim 11, wherein displaying the digitized slide comprises: receiving a user interaction associated with the one or more regions of interest of the displayed digitized slide; generating slide data as a function of the user interaction; and displaying the slide data attached to the digitized slide.

16; 16; 16

edspap.20250104847