SegmentationMetrics

Computes classical quality metrics for assessing the quality of a segmentation relatively to a ground truth.

Access to parameter description

This algorithm allows the comparison of a segmented image with a ground truth in order to assess the accuracy of a segmentation process; for instance, a classification by machine learning.

It outputs several statistical indicators, which can be computed independently for each label of the input images, or globally to provide a unique set of scores:

The number of positive results in the input image that are defined as such in the ground truth image (TP).
The number of positive results in the input image that are not defined as such in the ground truth image (FP).
The number of negative results in the input image that are defined as such in the ground truth image (TN).
The number of negative results in the input image that are not defined as such in the ground truth image (FN).
The sensitivity, which measures the proportion of actual positives that are correctly identified as such. This metric is also called recall or true positive rate. $$Sensitivity = Recall = TPR = \frac{TP}{TP+FN}$$
The specificity, which measures the proportion of actual negatives that are correctly identified as such (true negative rate). $$ Specificity = TNR = \frac{TN}{TN+FP}$$
The precision, which measures the fraction of relevant results (true positives) among positive results (true and false positives). $$ Precision = \frac{TP}{TP+FP}$$
The accuracy, which measures the percentage of correct classifications among all results. $$ Accuracy = \frac{TP+TN}{TP+FN+TN+FP} $$
The Dice coefficient, which is a statistic used for comparing the similarity of two samples (Sørensen-Dice coefficient).This metric is also called $F_1$ score. $$ Dice = F_1 = \frac{2TP}{2TP+FP+FN} $$
The Jaccard index, also called mean Intersection-Over-Union metric. $$ Jaccard = IoU = \frac{TP}{TP+FP+FN} $$

See also

Function Syntax

This function returns a SegmentationMetricsOutput structure containing outputClassMeasurement, outputMicroMeasurement and outputMacroMeasurement.

// Output structure of the segmentationMetrics function.
struct SegmentationMetricsOutput final
{
    /// The similarity results. The metrics are given for each label of the input image.
    SegmentationMetricsObjectMsr::Ptr outputClassMeasurement;
    /// The similarity results. The metrics are given globally for the input image.
    SegmentationMetricsMicroMsr::Ptr outputMicroMeasurement;
    /// The similarity results. The metrics are given globally for the input image by macro-averaging.
    SegmentationMetricsMacroMsr::Ptr outputMacroMeasurement;
};

// Function prototype
SegmentationMetricsOutput
segmentationMetrics( std::shared_ptr< iolink::ImageView > inputObjectImage,
                     std::shared_ptr< iolink::ImageView > inputReferenceImage,
                     SegmentationMetrics::MetricScope metricScope );

This function returns a tuple containing output_class_measurement, output_micro_measurement and output_macro_measurement.

// Function prototype.
segmentation_metrics(input_object_image: idt.ImageType,
                     input_reference_image: idt.ImageType,
                     metric_scope: Union[Literal["PER_CLASS"],Literal["MICRO_AVERAGED"],Literal["MACRO_AVERAGED"],Literal["ALL_MODES"],SegmentationMetrics.MetricScope] = SegmentationMetrics.MetricScope.ALL_MODES) -> Tuple[SegmentationMetricsObjectMsr, SegmentationMetricsMicroMsr, SegmentationMetricsMacroMsr]

This function returns a SegmentationMetricsOutput structure containing outputClassMeasurement, outputMicroMeasurement and outputMacroMeasurement.

/// Output structure of the SegmentationMetrics function.
public struct SegmentationMetricsOutput
{
    /// 
    /// The similarity results. The metrics are given for each label of the input image.
    /// 
    public SegmentationMetricsObjectMsr outputClassMeasurement;
    /// The similarity results. The metrics are given globally for the input image.
    public SegmentationMetricsMicroMsr outputMicroMeasurement;
    /// 
    /// The similarity results. The metrics are given globally for the input image by macro-averaging.
    /// 
    public SegmentationMetricsMacroMsr outputMacroMeasurement;
};

// Function prototype.
public static SegmentationMetricsOutput
SegmentationMetrics( IOLink.ImageView inputObjectImage,
                     IOLink.ImageView inputReferenceImage,
                     SegmentationMetrics.MetricScope metricScope = ImageDev.SegmentationMetrics.MetricScope.ALL_MODES );

Class Syntax

// Command constructor.
SegmentationMetrics();

/// Gets the inputObjectImage parameter.
/// The input image. It can be a binary or a label image.
std::shared_ptr< iolink::ImageView > inputObjectImage() const;
/// Sets the inputObjectImage parameter.
/// The input image. It can be a binary or a label image.
void setInputObjectImage( std::shared_ptr< iolink::ImageView > inputObjectImage );

/// Gets the inputReferenceImage parameter.
/// The ground truth image. It can be a binary or a label image. It must have same shape and interpretation as the first input image.
std::shared_ptr< iolink::ImageView > inputReferenceImage() const;
/// Sets the inputReferenceImage parameter.
/// The ground truth image. It can be a binary or a label image. It must have same shape and interpretation as the first input image.
void setInputReferenceImage( std::shared_ptr< iolink::ImageView > inputReferenceImage );

/// Gets the metricScope parameter.
/// The type of output measurement to compute.
SegmentationMetrics::MetricScope metricScope() const;
/// Sets the metricScope parameter.
/// The type of output measurement to compute.
void setMetricScope( const SegmentationMetrics::MetricScope& metricScope );

/// Gets the outputClassMeasurement parameter.
/// The similarity results. The metrics are given for each label of the input image.
SegmentationMetricsObjectMsr::Ptr outputClassMeasurement() const;

/// Gets the outputMicroMeasurement parameter.
/// The similarity results. The metrics are given globally for the input image.
SegmentationMetricsMicroMsr::Ptr outputMicroMeasurement() const;

/// Gets the outputMacroMeasurement parameter.
/// The similarity results. The metrics are given globally for the input image by macro-averaging.
SegmentationMetricsMacroMsr::Ptr outputMacroMeasurement() const;


// Method to launch the command.
void execute();

# Property of the inputObjectImage parameter.
SegmentationMetrics.input_object_image
# Property of the inputReferenceImage parameter.
SegmentationMetrics.input_reference_image
# Property of the metricScope parameter.
SegmentationMetrics.metric_scope
# Property of the outputClassMeasurement parameter.
SegmentationMetrics.output_class_measurement
# Property of the outputMicroMeasurement parameter.
SegmentationMetrics.output_micro_measurement
# Property of the outputMacroMeasurement parameter.
SegmentationMetrics.output_macro_measurement

// Method to launch the command.
execute()

// Command constructor.
SegmentationMetrics()

// Property of the inputObjectImage parameter.
SegmentationMetrics.inputObjectImage
// Property of the inputReferenceImage parameter.
SegmentationMetrics.inputReferenceImage
// Property of the metricScope parameter.
SegmentationMetrics.metricScope
// Property of the outputClassMeasurement parameter.
SegmentationMetrics.outputClassMeasurement
// Property of the outputMicroMeasurement parameter.
SegmentationMetrics.outputMicroMeasurement
// Property of the outputMacroMeasurement parameter.
SegmentationMetrics.outputMacroMeasurement

// Method to launch the command.
Execute()

Parameters

Parameter Name

Description

Type

Supported Values

Default Value

inputObjectImage

The input image. It can be a binary or a label image.

Image

Binary or Label

nullptr

inputReferenceImage

The ground truth image. It can be a binary or a label image. It must have same shape and interpretation as the first input image.

Image

Binary or Label

nullptr

metricScope

The type of output measurement to compute.

Value	Description
PER_CLASS	The segmentation metrics are only computed for each label of the input images. Only the object measurement output is filled.
MICRO_AVERAGED	The segmentation metrics are computed globally on the input images by macro-averaging. The true positives, false positives, true negatives, and false negatives, scores are aggregated over all classes. The different metrics are deduced from these cumulated scores. Only the image measurement output is filled.
MACRO_AVERAGED	The segmentation metrics are computed globally on the input images by macro-averaging. The metric scores are averaged over all classes without being weighted by the probability of presence of each class, except for the accuracy, which is computed identically to the micro-averaged mode. Only the macro measurement output is filled.
ALL_MODES	The segmentation metrics are computed globally and for each label of the input images. Both output measurement types are filled.

Enumeration

ALL_MODES

outputClassMeasurement

The similarity results. The metrics are given for each label of the input image.

SegmentationMetricsObjectMsr

nullptr

outputMicroMeasurement

The similarity results. The metrics are given globally for the input image.

SegmentationMetricsMicroMsr

nullptr

outputMacroMeasurement

The similarity results. The metrics are given globally for the input image by macro-averaging.

SegmentationMetricsMacroMsr

nullptr

Parameter Name

Description

Type

Supported Values

Default Value

input_object_image

The input image. It can be a binary or a label image.

image

Binary or Label

None

input_reference_image

The ground truth image. It can be a binary or a label image. It must have same shape and interpretation as the first input image.

image

Binary or Label

None

metric_scope

The type of output measurement to compute.

Value	Description
PER_CLASS	The segmentation metrics are only computed for each label of the input images. Only the object measurement output is filled.
MICRO_AVERAGED	The segmentation metrics are computed globally on the input images by macro-averaging. The true positives, false positives, true negatives, and false negatives, scores are aggregated over all classes. The different metrics are deduced from these cumulated scores. Only the image measurement output is filled.
MACRO_AVERAGED	The segmentation metrics are computed globally on the input images by macro-averaging. The metric scores are averaged over all classes without being weighted by the probability of presence of each class, except for the accuracy, which is computed identically to the micro-averaged mode. Only the macro measurement output is filled.
ALL_MODES	The segmentation metrics are computed globally and for each label of the input images. Both output measurement types are filled.

enumeration

ALL_MODES

output_class_measurement

The similarity results. The metrics are given for each label of the input image.

SegmentationMetricsObjectMsr

None

output_micro_measurement

The similarity results. The metrics are given globally for the input image.

SegmentationMetricsMicroMsr

None

output_macro_measurement

The similarity results. The metrics are given globally for the input image by macro-averaging.

SegmentationMetricsMacroMsr

None

Parameter Name

Description

Type

Supported Values

Default Value

inputObjectImage

The input image. It can be a binary or a label image.

Image

Binary or Label

null

inputReferenceImage

The ground truth image. It can be a binary or a label image. It must have same shape and interpretation as the first input image.

Image

Binary or Label

null

metricScope

The type of output measurement to compute.

Value	Description
PER_CLASS	The segmentation metrics are only computed for each label of the input images. Only the object measurement output is filled.
MICRO_AVERAGED	The segmentation metrics are computed globally on the input images by macro-averaging. The true positives, false positives, true negatives, and false negatives, scores are aggregated over all classes. The different metrics are deduced from these cumulated scores. Only the image measurement output is filled.
MACRO_AVERAGED	The segmentation metrics are computed globally on the input images by macro-averaging. The metric scores are averaged over all classes without being weighted by the probability of presence of each class, except for the accuracy, which is computed identically to the micro-averaged mode. Only the macro measurement output is filled.
ALL_MODES	The segmentation metrics are computed globally and for each label of the input images. Both output measurement types are filled.

Enumeration

ALL_MODES

outputClassMeasurement

The similarity results. The metrics are given for each label of the input image.

SegmentationMetricsObjectMsr

null

outputMicroMeasurement

The similarity results. The metrics are given globally for the input image.

SegmentationMetricsMicroMsr

null

outputMacroMeasurement

The similarity results. The metrics are given globally for the input image by macro-averaging.

SegmentationMetricsMacroMsr

null

Object Examples

auto polystyrene_sep_label = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene_sep_label.vip" );

SegmentationMetrics segmentationMetricsAlgo;
segmentationMetricsAlgo.setInputObjectImage( polystyrene_sep_label );
segmentationMetricsAlgo.setInputReferenceImage( polystyrene_sep_label );
segmentationMetricsAlgo.setMetricScope( SegmentationMetrics::MetricScope::ALL_MODES );
segmentationMetricsAlgo.execute();

std::cout << "labelValue: " << segmentationMetricsAlgo.outputClassMeasurement()->labelValue( 0 , 0 ) ;
std::cout << "truePositive: " << segmentationMetricsAlgo.outputMicroMeasurement()->truePositive( 0 ) ;
std::cout << "sensitivity: " << segmentationMetricsAlgo.outputMacroMeasurement()->sensitivity( 0 ) ;

polystyrene_sep_label = imagedev.read_vip_image(imagedev_data.get_image_path("polystyrene_sep_label.vip"))

segmentation_metrics_algo = imagedev.SegmentationMetrics()
segmentation_metrics_algo.input_object_image = polystyrene_sep_label
segmentation_metrics_algo.input_reference_image = polystyrene_sep_label
segmentation_metrics_algo.metric_scope = imagedev.SegmentationMetrics.ALL_MODES
segmentation_metrics_algo.execute()

print("labelValue: ", str(segmentation_metrics_algo.output_class_measurement.label_value(0, 0)))
print("truePositive: ", str(segmentation_metrics_algo.output_micro_measurement.true_positive(0)))
print("sensitivity: ", str(segmentation_metrics_algo.output_macro_measurement.sensitivity(0)))

ImageView polystyrene_sep_label = Data.ReadVipImage( @"Data/images/polystyrene_sep_label.vip" );

SegmentationMetrics segmentationMetricsAlgo = new SegmentationMetrics
{
    inputObjectImage = polystyrene_sep_label,
    inputReferenceImage = polystyrene_sep_label,
    metricScope = SegmentationMetrics.MetricScope.ALL_MODES
};
segmentationMetricsAlgo.Execute();

Console.WriteLine( "labelValue: " + segmentationMetricsAlgo.outputClassMeasurement.labelValue( 0 , 0 ) );
Console.WriteLine( "truePositive: " + segmentationMetricsAlgo.outputMicroMeasurement.truePositive( 0 ) );
Console.WriteLine( "sensitivity: " + segmentationMetricsAlgo.outputMacroMeasurement.sensitivity( 0 ) );

Function Examples

auto polystyrene_sep_label = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene_sep_label.vip" );

auto result = segmentationMetrics( polystyrene_sep_label, polystyrene_sep_label, SegmentationMetrics::MetricScope::ALL_MODES );

std::cout << "labelValue: " << result.outputClassMeasurement->labelValue( 0 , 0 ) ;
std::cout << "truePositive: " << result.outputMicroMeasurement->truePositive( 0 ) ;
std::cout << "sensitivity: " << result.outputMacroMeasurement->sensitivity( 0 ) ;

polystyrene_sep_label = imagedev.read_vip_image(imagedev_data.get_image_path("polystyrene_sep_label.vip"))

result_output_class_measurement, result_output_micro_measurement, result_output_macro_measurement = imagedev.segmentation_metrics(polystyrene_sep_label, polystyrene_sep_label, imagedev.SegmentationMetrics.ALL_MODES)

print("labelValue: ", str(result_output_class_measurement.label_value(0, 0)))
print("truePositive: ", str(result_output_micro_measurement.true_positive(0)))
print("sensitivity: ", str(result_output_macro_measurement.sensitivity(0)))

ImageView polystyrene_sep_label = Data.ReadVipImage( @"Data/images/polystyrene_sep_label.vip" );

Processing.SegmentationMetricsOutput result = Processing.SegmentationMetrics( polystyrene_sep_label, polystyrene_sep_label, SegmentationMetrics.MetricScope.ALL_MODES );

Console.WriteLine(  "labelValue: " + result.outputClassMeasurement.labelValue( 0 , 0 )  );
Console.WriteLine(  "truePositive: " + result.outputMicroMeasurement.truePositive( 0 )  );
Console.WriteLine(  "sensitivity: " + result.outputMacroMeasurement.sensitivity( 0 )  );

SegmentationMetrics

- Function Syntax

+ Class Syntax

Parameters

Object Examples

Function Examples

Function Syntax

Class Syntax