segmetrics.detection

class segmetrics.detection.FalseMerge(aggregation: Literal['sum', 'mean', 'geometric-mean', 'object-mean'] = 'mean')

Bases: Measure

Counts falsely merged objects.

References:

• L. Coelho, A. Shariff, and R. Murphy, “Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms,” in Proc. Int. Symp. Biomed. Imag., 2009, pp. 518–521.

compute(actual: None) → List[float]

Computes the performance measure for the given segmentation results based on the previously set expected result.

Parameters:

actual – An image containing uniquely labeled object masks corresponding to the segmentation results.

default_name() → str

Returns the default name of this measure.

class segmetrics.detection.FalseNegative(**kwargs)

Bases: Measure

Counts missing objects.

References:

• L. Coelho, A. Shariff, and R. Murphy, “Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms,” in Proc. Int. Symp. Biomed. Imag., 2009, pp. 518–521.

compute(actual: None) → List[float]

Computes the performance measure for the given segmentation results based on the previously set expected result.

Parameters:

actual – An image containing uniquely labeled object masks corresponding to the segmentation results.

default_name() → str

Returns the default name of this measure.

class segmetrics.detection.FalsePositive(**kwargs)

Bases: Measure

Counts spurious objects.

References:

• L. Coelho, A. Shariff, and R. Murphy, “Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms,” in Proc. Int. Symp. Biomed. Imag., 2009, pp. 518–521.

compute(actual: None) → List[float]

Computes the performance measure for the given segmentation results based on the previously set expected result.

Parameters:

actual – An image containing uniquely labeled object masks corresponding to the segmentation results.

default_name() → str

Returns the default name of this measure.

class segmetrics.detection.FalseSplit(aggregation: Literal['sum', 'mean', 'geometric-mean', 'object-mean'] = 'mean')

Bases: Measure

Counts falsely split objects.

References:

• L. Coelho, A. Shariff, and R. Murphy, “Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms,” in Proc. Int. Symp. Biomed. Imag., 2009, pp. 518–521.

compute(actual: None) → List[float]

Computes the performance measure for the given segmentation results based on the previously set expected result.

Parameters:

actual – An image containing uniquely labeled object masks corresponding to the segmentation results.

default_name() → str

Returns the default name of this measure.