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| author | CoprDistGit <infra@openeuler.org> | 2023-05-15 07:59:38 +0000 |
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| committer | CoprDistGit <infra@openeuler.org> | 2023-05-15 07:59:38 +0000 |
| commit | c2ff162dcb4478e759ed7d15e7ccd7833bfdab26 (patch) | |
| tree | 790ee67ea303cf9d6e9d72908cafc884f5575463 | |
| parent | b48b50bf19369ad8552a46cf611b8b45298977bf (diff) | |
automatic import of python-seg-metrics
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-seg-metrics.spec | 777 | ||||
| -rw-r--r-- | sources | 1 |
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@@ -0,0 +1 @@ +/seg_metrics-1.1.3.tar.gz diff --git a/python-seg-metrics.spec b/python-seg-metrics.spec new file mode 100644 index 0000000..777613b --- /dev/null +++ b/python-seg-metrics.spec @@ -0,0 +1,777 @@ +%global _empty_manifest_terminate_build 0 +Name: python-seg-metrics +Version: 1.1.3 +Release: 1 +Summary: A package to compute different segmentation metrics for 2D/3D medical images. +License: MIT License +URL: https://github.com/Ordgod/segmentation_metrics +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e2/2a/609b24ca37b73776253bb8e5a120d0f5efa41f6a030e23d889fe9554729a/seg_metrics-1.1.3.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-numpy +Requires: python3-coverage +Requires: python3-matplotlib +Requires: python3-parameterized +Requires: python3-tqdm +Requires: python3-medutils +Requires: python3-PySimpleGUI +Requires: python3-SimpleITK + +%description +# Segmentaion Metrics Package [](https://zenodo.org/badge/latestdoi/273067948) + + +[](https://codecov.io/gh/Jingnan-Jia/segmentation_metrics) + +[](https://www.oscs1024.com/project/Jingnan-Jia/segmentation_metrics?ref=badge_small) + +This is a simple package to compute different metrics for **Medical** image segmentation(images with suffix `.mhd`, `.mha`, `.nii`, `.nii.gz` or `.nrrd`), and write them to csv file. + +## Summary +To assess the segmentation performance, there are several different methods. Two main methods are volume-based metrics and distance-based metrics. + +## Metrics included +This library computes the following performance metrics for segmentation: + +### Voxel based metrics +- Dice (F-1) +- Jaccard +- Precision +- Recall +- False positive rate +- False negtive rate +- Volume similarity + + +The equations for these metrics can be seen in the [wikipedia](https://en.wikipedia.org/wiki/Precision_and_recall). + +### Surface Distance based metrics (with spacing as default) +- [Hausdorff distance](https://en.wikipedia.org/wiki/Hausdorff_distance) +- Hausdorff distance 95% percentile +- Mean (Average) surface distance +- Median surface distance +- Std surface distance + +**Note**: These metrics are **symmetric**, which means the distance from A to B is the same as the distance from B to A. + +For each contour voxel of the segmented volume (A), the Euclidean distance from the closest contour voxel of the reference volume (B) is computed and stored as `list1`. This computation is also performed for the contour voxels of the reference volume (B), stored as `list2`. `list1` and `list2` are merged to get `list3`. +- `Hausdorff distance` is the maximum value of `list3`. +- `Hausdorff distance 95% percentile` is the 95% percentile of `list3`. +- `Mean (Average) surface distance` is the mean value of `list3`. +- `Median surface distance` is the median value of `list3`. +- `Std surface distance` is the standard deviation of `list3`. + +**References:** +1. Heimann T, Ginneken B, Styner MA, et al. Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets. IEEE Transactions on Medical Imaging. 2009;28(8):1251–1265. +2. Yeghiazaryan, Varduhi, and Irina D. Voiculescu. "Family of boundary overlap metrics for the evaluation of medical image segmentation." Journal of Medical Imaging 5.1 (2018): 015006. +3. Ruskó, László, György Bekes, and Márta Fidrich. "Automatic segmentation of the liver from multi-and single-phase contrast-enhanced CT images." Medical Image Analysis 13.6 (2009): 871-882. + +## Installation + +```shell +$ pip install seg-metrics +``` + +## Usage +At first, import the package: +```python +import seg_metrics.seg_metrics as sg +``` + + +### Evaluate two batches of images with same filenames from two different folders +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_path = 'data/gdth' # this folder saves a batch of ground truth images +pred_path = 'data/pred' # this folder saves the same number of prediction images +csv_file = 'metrics.csv' # results will be saved to this file and prented on terminal as well. If not set, results +# will only be shown on terminal. + +metrics = sg.write_metrics(labels=labels[1:], # exclude background + gdth_path=gdth_path, + pred_path=pred_path, + csv_file=csv_file) +print(metrics) # a list of dictionaries which includes the metrics for each pair of image. +``` +After runing the above codes, you can get a **list of dictionaries** `metrics` which contains all the metrics. **Also you can find a `.csv` file containing all metrics in the same directory.** If the `csv_file` is not given, the metrics results will not be saved to disk. + +### Evaluate two images +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_file = 'data/gdth.mhd' # ground truth image full path +pred_file = 'data/pred.mhd' # prediction image full path +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels=labels[1:], # exclude background + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file) +``` +After runing the above codes, you can get a **dictionary** `metrics` which contains all the metrics. **Also you can find a `.csv` file containing all metrics in the same directory.** + +**Note:** +1. When evaluating one image, the returned `metrics` is a dictionary. +2. When evaluating a batch of images, the returned `metrics` is a list of dictionaries. + +### Evaluate two images with specific metrics +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_file = 'data/gdth.mhd' +pred_file = 'data/pred.mhd' +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file, + metrics=['dice', 'hd']) +# for only one metric +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file, + metrics='msd') +``` + +By passing the following parameters to select specific metrics. + +```python +- dice: Dice (F-1) +- jaccard: Jaccard +- precision: Precision +- recall: Recall +- fpr: False positive rate +- fnr: False negtive rate +- vs: Volume similarity + +- hd: Hausdorff distance +- hd95: Hausdorff distance 95% percentile +- msd: Mean (Average) surface distance +- mdsd: Median surface distance +- stdsd: Std surface distance +``` + +For example: +```python +labels = [1] +gdth_file = 'data/gdth.mhd' +pred_file = 'data/pred.mhd' +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels, gdth_file, pred_file, csv_file, metrics=['dice', 'hd95']) +dice = metrics['dice'] +hd95 = metrics['hd95'] +``` + + +### Evaluate two images in memory instead of disk +**Note:** +1. The two images must be both numpy.ndarray or SimpleITK.Image. +2. Input arguments are different. Please use `gdth_img` and `pred_img` instead of `gdth_path` and `pred_path`. +3. If evaluating `numpy.ndarray`, the default `spacing` for all dimensions would be `1.0` for distance based metrics. +4. If you want to evaluate `numpy.ndarray` with specific spacing, pass a sequence with the length of image dimension as `spacing`. + +```python +labels = [0, 1, 2] +gdth_img = np.array([[0,0,1], + [0,1,2]]) +pred_img = np.array([[0,0,1], + [0,2,2]]) +csv_file = 'metrics.csv' +spacing = [1, 2] +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + spacing=spacing, + metrics=['dice', 'hd']) +# for only one metrics +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + spacing=spacing, + metrics='msd') +``` + +#### About the calculation of surface distance +The default surface distance is calculated based on **fullyConnected** border. To change the default connected type, +you can set argument `fullyConnected` as `False` as follows. +```python +metrics = sg.write_metrics(labels=[1,2,3], + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + fully_connected=False) +``` +In 2D image, fullyconnected means 8 neighbor points, while faceconnected means 4 neighbor points. +In 3D image, fullyconnected means 26 neighbor points, while faceconnected means 6 neighbor points. + + +# How to obtain more metrics? like "False omission rate" or "Accuracy"? +A great number of different metrics, like "False omission rate" or "Accuracy", could be derived from some the [confusion matrics](https://en.wikipedia.org/wiki/Confusion_matrix). To calculate more metrics or design custom metrics, use `TPTNFPFN=True` to return the number of voxels/pixels of true positive (TP), true negative (TN), false positive (FP), false negative (FN) predictions. For example, +```python +metrics = sg.write_metrics( + gdth_img=gdth_img, + pred_img=pred_img, + TPTNFPFN=True) +tp, tn, fp, fn = metrics['TP'], metrics['TN'], metrics['FP'], metrics['FN'] +false_omission_rate = fn/(fn+tn) +accuracy = (tp + tn)/(tp + tn + fp + fn) +``` + +# Comparision with medpy +`medpy` also provide functions to calculate metrics for medical images. But `seg-metrics` +has several advantages. +1. **Faster**. `seg-metrics` is **10 times faster** calculating distance based metrics. This [jupyter +notebook](https://colab.research.google.com/drive/1gLQghS1d_fWsaJs3G4Ip0GlZHEJFcxDr#scrollTo=mDWvyxW7VExd) could reproduce the results. +2. **More convenient**. `seg-metrics` can calculate all different metrics in once in one function while +`medpy` needs to call different functions multiple times which cost more time and code. +3. **More Powerful**. `seg-metrics` can calculate **multi-label** segmentation metrics and save results to +`.csv` file in good manner, but `medpy` only provides binary segmentation metrics. Comparision can be found in this [jupyter +notebook](https://colab.research.google.com/drive/1gLQghS1d_fWsaJs3G4Ip0GlZHEJFcxDr#scrollTo=mDWvyxW7VExd). + + + +If this repository helps you in anyway, show your love ❤️ by putting a ⭐ on this project. +I would also appreciate it if you cite the package in your publication. (**Note:** This package is **NOT** approved for clinical use and is intended for research use only. ) + +#Bibtex + + @misc{Jingnan, + title = {A package to compute segmentation metrics: seg-metrics}, + author = {Jingnan Jia}, + url = {https://github.com/Ordgod/segmentation_metrics}, + year = {2020}, + doi = {10.5281/zenodo.3995075} + } + + + + + + + +%package -n python3-seg-metrics +Summary: A package to compute different segmentation metrics for 2D/3D medical images. +Provides: python-seg-metrics +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-seg-metrics +# Segmentaion Metrics Package [](https://zenodo.org/badge/latestdoi/273067948) + + +[](https://codecov.io/gh/Jingnan-Jia/segmentation_metrics) + +[](https://www.oscs1024.com/project/Jingnan-Jia/segmentation_metrics?ref=badge_small) + +This is a simple package to compute different metrics for **Medical** image segmentation(images with suffix `.mhd`, `.mha`, `.nii`, `.nii.gz` or `.nrrd`), and write them to csv file. + +## Summary +To assess the segmentation performance, there are several different methods. Two main methods are volume-based metrics and distance-based metrics. + +## Metrics included +This library computes the following performance metrics for segmentation: + +### Voxel based metrics +- Dice (F-1) +- Jaccard +- Precision +- Recall +- False positive rate +- False negtive rate +- Volume similarity + + +The equations for these metrics can be seen in the [wikipedia](https://en.wikipedia.org/wiki/Precision_and_recall). + +### Surface Distance based metrics (with spacing as default) +- [Hausdorff distance](https://en.wikipedia.org/wiki/Hausdorff_distance) +- Hausdorff distance 95% percentile +- Mean (Average) surface distance +- Median surface distance +- Std surface distance + +**Note**: These metrics are **symmetric**, which means the distance from A to B is the same as the distance from B to A. + +For each contour voxel of the segmented volume (A), the Euclidean distance from the closest contour voxel of the reference volume (B) is computed and stored as `list1`. This computation is also performed for the contour voxels of the reference volume (B), stored as `list2`. `list1` and `list2` are merged to get `list3`. +- `Hausdorff distance` is the maximum value of `list3`. +- `Hausdorff distance 95% percentile` is the 95% percentile of `list3`. +- `Mean (Average) surface distance` is the mean value of `list3`. +- `Median surface distance` is the median value of `list3`. +- `Std surface distance` is the standard deviation of `list3`. + +**References:** +1. Heimann T, Ginneken B, Styner MA, et al. Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets. IEEE Transactions on Medical Imaging. 2009;28(8):1251–1265. +2. Yeghiazaryan, Varduhi, and Irina D. Voiculescu. "Family of boundary overlap metrics for the evaluation of medical image segmentation." Journal of Medical Imaging 5.1 (2018): 015006. +3. Ruskó, László, György Bekes, and Márta Fidrich. "Automatic segmentation of the liver from multi-and single-phase contrast-enhanced CT images." Medical Image Analysis 13.6 (2009): 871-882. + +## Installation + +```shell +$ pip install seg-metrics +``` + +## Usage +At first, import the package: +```python +import seg_metrics.seg_metrics as sg +``` + + +### Evaluate two batches of images with same filenames from two different folders +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_path = 'data/gdth' # this folder saves a batch of ground truth images +pred_path = 'data/pred' # this folder saves the same number of prediction images +csv_file = 'metrics.csv' # results will be saved to this file and prented on terminal as well. If not set, results +# will only be shown on terminal. + +metrics = sg.write_metrics(labels=labels[1:], # exclude background + gdth_path=gdth_path, + pred_path=pred_path, + csv_file=csv_file) +print(metrics) # a list of dictionaries which includes the metrics for each pair of image. +``` +After runing the above codes, you can get a **list of dictionaries** `metrics` which contains all the metrics. **Also you can find a `.csv` file containing all metrics in the same directory.** If the `csv_file` is not given, the metrics results will not be saved to disk. + +### Evaluate two images +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_file = 'data/gdth.mhd' # ground truth image full path +pred_file = 'data/pred.mhd' # prediction image full path +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels=labels[1:], # exclude background + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file) +``` +After runing the above codes, you can get a **dictionary** `metrics` which contains all the metrics. **Also you can find a `.csv` file containing all metrics in the same directory.** + +**Note:** +1. When evaluating one image, the returned `metrics` is a dictionary. +2. When evaluating a batch of images, the returned `metrics` is a list of dictionaries. + +### Evaluate two images with specific metrics +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_file = 'data/gdth.mhd' +pred_file = 'data/pred.mhd' +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file, + metrics=['dice', 'hd']) +# for only one metric +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file, + metrics='msd') +``` + +By passing the following parameters to select specific metrics. + +```python +- dice: Dice (F-1) +- jaccard: Jaccard +- precision: Precision +- recall: Recall +- fpr: False positive rate +- fnr: False negtive rate +- vs: Volume similarity + +- hd: Hausdorff distance +- hd95: Hausdorff distance 95% percentile +- msd: Mean (Average) surface distance +- mdsd: Median surface distance +- stdsd: Std surface distance +``` + +For example: +```python +labels = [1] +gdth_file = 'data/gdth.mhd' +pred_file = 'data/pred.mhd' +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels, gdth_file, pred_file, csv_file, metrics=['dice', 'hd95']) +dice = metrics['dice'] +hd95 = metrics['hd95'] +``` + + +### Evaluate two images in memory instead of disk +**Note:** +1. The two images must be both numpy.ndarray or SimpleITK.Image. +2. Input arguments are different. Please use `gdth_img` and `pred_img` instead of `gdth_path` and `pred_path`. +3. If evaluating `numpy.ndarray`, the default `spacing` for all dimensions would be `1.0` for distance based metrics. +4. If you want to evaluate `numpy.ndarray` with specific spacing, pass a sequence with the length of image dimension as `spacing`. + +```python +labels = [0, 1, 2] +gdth_img = np.array([[0,0,1], + [0,1,2]]) +pred_img = np.array([[0,0,1], + [0,2,2]]) +csv_file = 'metrics.csv' +spacing = [1, 2] +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + spacing=spacing, + metrics=['dice', 'hd']) +# for only one metrics +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + spacing=spacing, + metrics='msd') +``` + +#### About the calculation of surface distance +The default surface distance is calculated based on **fullyConnected** border. To change the default connected type, +you can set argument `fullyConnected` as `False` as follows. +```python +metrics = sg.write_metrics(labels=[1,2,3], + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + fully_connected=False) +``` +In 2D image, fullyconnected means 8 neighbor points, while faceconnected means 4 neighbor points. +In 3D image, fullyconnected means 26 neighbor points, while faceconnected means 6 neighbor points. + + +# How to obtain more metrics? like "False omission rate" or "Accuracy"? +A great number of different metrics, like "False omission rate" or "Accuracy", could be derived from some the [confusion matrics](https://en.wikipedia.org/wiki/Confusion_matrix). To calculate more metrics or design custom metrics, use `TPTNFPFN=True` to return the number of voxels/pixels of true positive (TP), true negative (TN), false positive (FP), false negative (FN) predictions. For example, +```python +metrics = sg.write_metrics( + gdth_img=gdth_img, + pred_img=pred_img, + TPTNFPFN=True) +tp, tn, fp, fn = metrics['TP'], metrics['TN'], metrics['FP'], metrics['FN'] +false_omission_rate = fn/(fn+tn) +accuracy = (tp + tn)/(tp + tn + fp + fn) +``` + +# Comparision with medpy +`medpy` also provide functions to calculate metrics for medical images. But `seg-metrics` +has several advantages. +1. **Faster**. `seg-metrics` is **10 times faster** calculating distance based metrics. This [jupyter +notebook](https://colab.research.google.com/drive/1gLQghS1d_fWsaJs3G4Ip0GlZHEJFcxDr#scrollTo=mDWvyxW7VExd) could reproduce the results. +2. **More convenient**. `seg-metrics` can calculate all different metrics in once in one function while +`medpy` needs to call different functions multiple times which cost more time and code. +3. **More Powerful**. `seg-metrics` can calculate **multi-label** segmentation metrics and save results to +`.csv` file in good manner, but `medpy` only provides binary segmentation metrics. Comparision can be found in this [jupyter +notebook](https://colab.research.google.com/drive/1gLQghS1d_fWsaJs3G4Ip0GlZHEJFcxDr#scrollTo=mDWvyxW7VExd). + + + +If this repository helps you in anyway, show your love ❤️ by putting a ⭐ on this project. +I would also appreciate it if you cite the package in your publication. (**Note:** This package is **NOT** approved for clinical use and is intended for research use only. ) + +#Bibtex + + @misc{Jingnan, + title = {A package to compute segmentation metrics: seg-metrics}, + author = {Jingnan Jia}, + url = {https://github.com/Ordgod/segmentation_metrics}, + year = {2020}, + doi = {10.5281/zenodo.3995075} + } + + + + + + + +%package help +Summary: Development documents and examples for seg-metrics +Provides: python3-seg-metrics-doc +%description help +# Segmentaion Metrics Package [](https://zenodo.org/badge/latestdoi/273067948) + + +[](https://codecov.io/gh/Jingnan-Jia/segmentation_metrics) + +[](https://www.oscs1024.com/project/Jingnan-Jia/segmentation_metrics?ref=badge_small) + +This is a simple package to compute different metrics for **Medical** image segmentation(images with suffix `.mhd`, `.mha`, `.nii`, `.nii.gz` or `.nrrd`), and write them to csv file. + +## Summary +To assess the segmentation performance, there are several different methods. Two main methods are volume-based metrics and distance-based metrics. + +## Metrics included +This library computes the following performance metrics for segmentation: + +### Voxel based metrics +- Dice (F-1) +- Jaccard +- Precision +- Recall +- False positive rate +- False negtive rate +- Volume similarity + + +The equations for these metrics can be seen in the [wikipedia](https://en.wikipedia.org/wiki/Precision_and_recall). + +### Surface Distance based metrics (with spacing as default) +- [Hausdorff distance](https://en.wikipedia.org/wiki/Hausdorff_distance) +- Hausdorff distance 95% percentile +- Mean (Average) surface distance +- Median surface distance +- Std surface distance + +**Note**: These metrics are **symmetric**, which means the distance from A to B is the same as the distance from B to A. + +For each contour voxel of the segmented volume (A), the Euclidean distance from the closest contour voxel of the reference volume (B) is computed and stored as `list1`. This computation is also performed for the contour voxels of the reference volume (B), stored as `list2`. `list1` and `list2` are merged to get `list3`. +- `Hausdorff distance` is the maximum value of `list3`. +- `Hausdorff distance 95% percentile` is the 95% percentile of `list3`. +- `Mean (Average) surface distance` is the mean value of `list3`. +- `Median surface distance` is the median value of `list3`. +- `Std surface distance` is the standard deviation of `list3`. + +**References:** +1. Heimann T, Ginneken B, Styner MA, et al. Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets. IEEE Transactions on Medical Imaging. 2009;28(8):1251–1265. +2. Yeghiazaryan, Varduhi, and Irina D. Voiculescu. "Family of boundary overlap metrics for the evaluation of medical image segmentation." Journal of Medical Imaging 5.1 (2018): 015006. +3. Ruskó, László, György Bekes, and Márta Fidrich. "Automatic segmentation of the liver from multi-and single-phase contrast-enhanced CT images." Medical Image Analysis 13.6 (2009): 871-882. + +## Installation + +```shell +$ pip install seg-metrics +``` + +## Usage +At first, import the package: +```python +import seg_metrics.seg_metrics as sg +``` + + +### Evaluate two batches of images with same filenames from two different folders +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_path = 'data/gdth' # this folder saves a batch of ground truth images +pred_path = 'data/pred' # this folder saves the same number of prediction images +csv_file = 'metrics.csv' # results will be saved to this file and prented on terminal as well. If not set, results +# will only be shown on terminal. + +metrics = sg.write_metrics(labels=labels[1:], # exclude background + gdth_path=gdth_path, + pred_path=pred_path, + csv_file=csv_file) +print(metrics) # a list of dictionaries which includes the metrics for each pair of image. +``` +After runing the above codes, you can get a **list of dictionaries** `metrics` which contains all the metrics. **Also you can find a `.csv` file containing all metrics in the same directory.** If the `csv_file` is not given, the metrics results will not be saved to disk. + +### Evaluate two images +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_file = 'data/gdth.mhd' # ground truth image full path +pred_file = 'data/pred.mhd' # prediction image full path +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels=labels[1:], # exclude background + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file) +``` +After runing the above codes, you can get a **dictionary** `metrics` which contains all the metrics. **Also you can find a `.csv` file containing all metrics in the same directory.** + +**Note:** +1. When evaluating one image, the returned `metrics` is a dictionary. +2. When evaluating a batch of images, the returned `metrics` is a list of dictionaries. + +### Evaluate two images with specific metrics +```python +labels = [0, 4, 5 ,6 ,7 , 8] +gdth_file = 'data/gdth.mhd' +pred_file = 'data/pred.mhd' +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file, + metrics=['dice', 'hd']) +# for only one metric +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_path=gdth_file, + pred_path=pred_file, + csv_file=csv_file, + metrics='msd') +``` + +By passing the following parameters to select specific metrics. + +```python +- dice: Dice (F-1) +- jaccard: Jaccard +- precision: Precision +- recall: Recall +- fpr: False positive rate +- fnr: False negtive rate +- vs: Volume similarity + +- hd: Hausdorff distance +- hd95: Hausdorff distance 95% percentile +- msd: Mean (Average) surface distance +- mdsd: Median surface distance +- stdsd: Std surface distance +``` + +For example: +```python +labels = [1] +gdth_file = 'data/gdth.mhd' +pred_file = 'data/pred.mhd' +csv_file = 'metrics.csv' + +metrics = sg.write_metrics(labels, gdth_file, pred_file, csv_file, metrics=['dice', 'hd95']) +dice = metrics['dice'] +hd95 = metrics['hd95'] +``` + + +### Evaluate two images in memory instead of disk +**Note:** +1. The two images must be both numpy.ndarray or SimpleITK.Image. +2. Input arguments are different. Please use `gdth_img` and `pred_img` instead of `gdth_path` and `pred_path`. +3. If evaluating `numpy.ndarray`, the default `spacing` for all dimensions would be `1.0` for distance based metrics. +4. If you want to evaluate `numpy.ndarray` with specific spacing, pass a sequence with the length of image dimension as `spacing`. + +```python +labels = [0, 1, 2] +gdth_img = np.array([[0,0,1], + [0,1,2]]) +pred_img = np.array([[0,0,1], + [0,2,2]]) +csv_file = 'metrics.csv' +spacing = [1, 2] +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + spacing=spacing, + metrics=['dice', 'hd']) +# for only one metrics +metrics = sg.write_metrics(labels=labels[1:], # exclude background if needed + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + spacing=spacing, + metrics='msd') +``` + +#### About the calculation of surface distance +The default surface distance is calculated based on **fullyConnected** border. To change the default connected type, +you can set argument `fullyConnected` as `False` as follows. +```python +metrics = sg.write_metrics(labels=[1,2,3], + gdth_img=gdth_img, + pred_img=pred_img, + csv_file=csv_file, + fully_connected=False) +``` +In 2D image, fullyconnected means 8 neighbor points, while faceconnected means 4 neighbor points. +In 3D image, fullyconnected means 26 neighbor points, while faceconnected means 6 neighbor points. + + +# How to obtain more metrics? like "False omission rate" or "Accuracy"? +A great number of different metrics, like "False omission rate" or "Accuracy", could be derived from some the [confusion matrics](https://en.wikipedia.org/wiki/Confusion_matrix). To calculate more metrics or design custom metrics, use `TPTNFPFN=True` to return the number of voxels/pixels of true positive (TP), true negative (TN), false positive (FP), false negative (FN) predictions. For example, +```python +metrics = sg.write_metrics( + gdth_img=gdth_img, + pred_img=pred_img, + TPTNFPFN=True) +tp, tn, fp, fn = metrics['TP'], metrics['TN'], metrics['FP'], metrics['FN'] +false_omission_rate = fn/(fn+tn) +accuracy = (tp + tn)/(tp + tn + fp + fn) +``` + +# Comparision with medpy +`medpy` also provide functions to calculate metrics for medical images. But `seg-metrics` +has several advantages. +1. **Faster**. `seg-metrics` is **10 times faster** calculating distance based metrics. This [jupyter +notebook](https://colab.research.google.com/drive/1gLQghS1d_fWsaJs3G4Ip0GlZHEJFcxDr#scrollTo=mDWvyxW7VExd) could reproduce the results. +2. **More convenient**. `seg-metrics` can calculate all different metrics in once in one function while +`medpy` needs to call different functions multiple times which cost more time and code. +3. **More Powerful**. `seg-metrics` can calculate **multi-label** segmentation metrics and save results to +`.csv` file in good manner, but `medpy` only provides binary segmentation metrics. Comparision can be found in this [jupyter +notebook](https://colab.research.google.com/drive/1gLQghS1d_fWsaJs3G4Ip0GlZHEJFcxDr#scrollTo=mDWvyxW7VExd). + + + +If this repository helps you in anyway, show your love ❤️ by putting a ⭐ on this project. +I would also appreciate it if you cite the package in your publication. (**Note:** This package is **NOT** approved for clinical use and is intended for research use only. ) + +#Bibtex + + @misc{Jingnan, + title = {A package to compute segmentation metrics: seg-metrics}, + author = {Jingnan Jia}, + url = {https://github.com/Ordgod/segmentation_metrics}, + year = {2020}, + doi = {10.5281/zenodo.3995075} + } + + + + + + + +%prep +%autosetup -n seg-metrics-1.1.3 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-seg-metrics -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.3-1 +- Package Spec generated @@ -0,0 +1 @@ +51b445cdaacc221949624a877041f8b9 seg_metrics-1.1.3.tar.gz |