Abstract: Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

Abstract: Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

Abstract: Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

Abstract: Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

Abstract: Methods and systems are disclosed for segmenting an image. First and second frames of image data are generated at different times. A first portion of the first frame is compared to image data of the second frame, and a second portion of the second frame is selected based on the comparison. A displacement vector between the first portion and the second portion is calculated, where the displacement vector represents relative movement over time between the image data represented by the first portion and the image data represented by the second portion. An image is output with an indicator, and the location of the indicator on the image is determined by using the calculated displacement vector. The indicator can serve to distinguish between items in an imaging view.

Abstract: Methods and systems are disclosed for segmenting an image. First and second frames of image data are generated at different times. A first portion of the first frame is compared to image data of the second frame, and a second portion of the second frame is selected based on the comparison. A displacement vector between the first portion and the second portion is calculated, where the displacement vector represents relative movement over time between the image data represented by the first portion and the image data represented by the second portion. An image is output with an indicator, and the location of the indicator on the image is determined by using the calculated displacement vector. The indicator can serve to distinguish between items in an imaging view.

Abstract: Methods and systems are disclosed for segmenting an image. First and second frames of image data are generated at different times. A first portion of the first frame is compared to image data of the second frame, and a second portion of the second frame is selected based on the comparison. A displacement vector between the first portion and the second portion is calculated, where the displacement vector represents relative movement over time between the image data represented by the first portion and the image data represented by the second portion. An image is output with an indicator, and the location of the indicator on the image is determined by using the calculated displacement vector. The indicator can serve to distinguish between items in an imaging view.

Abstract: The invention provides methods of performing a sizing analysis. In the methods, a sizing ladder used in performing the sizing analysis is corrected. In one method, the sizing ladder is corrected for batch-to-batch variations in a sieving gel. In another method, the sizing ladder is corrected for a sample concentration that is different from the archival sizing ladder concentration. Methods are also provided in which the sizing ladder is corrected using a standard marker in a sample and/or using a real-time standard sizing ladder. The methods may be used individually or in combination.

Abstract: The invention provides methods of performing a sizing analysis. In the methods, a sizing ladder used in performing the sizing analysis is corrected. In one method, the sizing ladder is corrected for batch-to-batch variations in a sieving gel. In another method, the sizing ladder is corrected for a sample concentration that is different from the archival sizing ladder concentration. Methods are also provided in which the sizing ladder is corrected using a standard marker in a sample and/or using a real-time standard sizing ladder. The methods may be used individually or in combination.

Abstract: The invention provides methods of performing a sizing analysis. In the methods, a sizing ladder used in performing the sizing analysis is corrected. In one method, the sizing ladder is corrected for batch-to-batch variations in a sieving gel. In another method, the sizing ladder is corrected for a sample concentration that is different from the archival sizing ladder concentration. Methods are also provided in which the sizing ladder is corrected using a standard marker in a sample and/or using a real-time standard sizing ladder. The methods may be used individually or in combination.

Abstract: The invention provides methods of performing a sizing analysis. In the methods, a sizing ladder used in performing the sizing analysis is corrected. In one method, the sizing ladder is corrected for batch-to-batch variations in a sieving gel. In another method, the sizing ladder is corrected for a sample concentration that is different from the archival sizing ladder concentration. Methods are also provided in which the sizing ladder is corrected using a standard marker in a sample and/or using a real-time standard sizing ladder. The methods may be used individually or in combination.

Abstract: Methods for reducing fluid carryover by microfluidic devices including capillary element and/or fluid motion. Capillary elements coated with hydrophobic or hydrophilic coatings to resist fluid carryover are also provided. Microfluidic device handling systems are additionally included.