APPARATUS AND METHOD FOR IDENTIFYING REGIONS OF INTEREST DURING SLIDE DIGITIZATION
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.
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.