예제 #1
0
def init_evaluator(directory: str,
                   result_file_name: str = 'results.csv') -> eval_.Evaluator:
    """Initializes an evaluator.

    Args:
        directory (str): The directory for the results file.
        result_file_name (str): The result file name (CSV file).

    Returns:
        eval.Evaluator: An evaluator.
    """
    os.makedirs(
        directory,
        exist_ok=True)  # generate result directory, if it does not exists

    evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
    evaluator.add_writer(
        eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name)))
    evaluator.add_label(1, 'WhiteMatter')
    evaluator.add_label(2, 'GreyMatter')
    evaluator.add_label(3, 'Hippocampus')
    evaluator.add_label(4, 'Amygdala')
    evaluator.add_label(5, 'Thalamus')
    evaluator.metrics = [
        metric.DiceCoefficient(),
        metric.HausdorffDistance(95)
    ]  # Solutions
    # todo: add hausdorff distance, 95th percentile (see metric.HausdorffDistance)
    # evaluator.add_metric(metric.HausdorffDistance(95))
    # warnings.warn('Initialized evaluation with the Dice coefficient. Do you know other suitable metrics?')
    return evaluator
예제 #2
0
def init_evaluator(directory: str,
                   result_file_name: str = 'results.csv') -> eval_.Evaluator:
    """Initializes an evaluator.

    Args:
        directory (str): The directory for the results file.
        result_file_name (str): The result file name (CSV file).

    Returns:
        eval.Evaluator: An evaluator.
    """
    os.makedirs(
        directory,
        exist_ok=True)  # generate result directory, if it does not exists

    evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
    evaluator.add_writer(
        eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name)))
    evaluator.add_label(1, "WhiteMatter")
    evaluator.add_label(2, "GreyMatter")
    evaluator.add_label(3, "Hippocampus")
    evaluator.add_label(4, "Amygdala")
    evaluator.add_label(5, "Thalamus")
    evaluator.metrics = [metric.DiceCoefficient()]
    return evaluator
예제 #3
0
def main():

    print('Welcome to MIALab 2018!')

    # --- scikit-learn
    clf = sk_ensemble.RandomForestClassifier(max_depth=2, random_state=0)

    # --- SimpleITK
    image = sitk.Image(256, 128, 64, sitk.sitkInt16)
    print('Image dimension:', image.GetDimension())
    print('Voxel intensity before setting:', image.GetPixel(0, 0, 0))
    image.SetPixel(0, 0, 0, 1)
    print('Voxel intensity after setting:', image.GetPixel(0, 0, 0))

    # --- numpy and matplotlib
    array = np.array([1, 23, 2, 4])
    plt.plot(array)
    plt.ylabel('Some meaningful numbers')
    plt.xlabel('The x-axis')
    plt.title('Wohoo')

    plt.show()

    # --- pydensecrf
    d = crf.DenseCRF(1000, 2)

    # --- pymia
    eval = pymia_eval.Evaluator()

    print('Everything seems to work fine!')
def init_evaluator(directory: str, result_file_name: str = 'results.csv') -> eval_.Evaluator:
    """Initializes an evaluator.

    Args:
        directory (str): The directory for the results file.
        result_file_name (str): The result file name (CSV file).

    Returns:
        eval.Evaluator: An evaluator.
    """
    os.makedirs(directory, exist_ok=True)  # generate result directory, if it does not exists

    evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
    evaluator.add_writer(eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name)))
    evaluator.add_label(1, "WhiteMatter")
    evaluator.add_label(2, "GreyMatter")
    evaluator.add_label(3, "Hippocampus")
    evaluator.add_label(4, "Amygdala")
    evaluator.add_label(5, "Thalamus")
    evaluator.metrics = [metric.DiceCoefficient(),
                         metric.AreaUnderCurve(),
                         metric.VolumeSimilarity(),
                         metric.Accuracy(),
                         metric.AverageDistance(),
                         metric.CohenKappaMetric(),
                         metric.FalseNegative(),
                         metric.FalsePositive(),
                         metric.Fallout(),
                         metric.GroundTruthArea(),
                         metric.GroundTruthVolume(),
                         metric.Specificity(),
                         metric.Sensitivity()
                         ]
    return evaluator
예제 #5
0
파일: main.py 프로젝트: woshituobaye/pymia
def init_evaluator() -> pymia_eval.Evaluator:
    evaluator = pymia_eval.Evaluator(pymia_eval.ConsoleEvaluatorWriter(5))
    evaluator.add_label(1, 'Structure 1')
    evaluator.add_label(2, 'Structure 2')
    evaluator.add_label(3, 'Structure 3')
    evaluator.add_label(4, 'Structure 4')
    evaluator.metrics = [pymia_metric.DiceCoefficient()]
    return evaluator
예제 #6
0
def init_evaluator(csv_file: str=None):
    evaluator = pymia_eval.Evaluator(pymia_eval.ConsoleEvaluatorWriter(5))
    if csv_file is not None:
        evaluator.add_writer(pymia_eval.CSVEvaluatorWriter(csv_file))
    evaluator.add_writer(EvaluatorAggregator())
    evaluator.metrics = [pymia_metric.DiceCoefficient()]
    evaluator.add_label(1, "WhiteMatter")
    evaluator.add_label(2, "GreyMatter")
    evaluator.add_label(3, "Hippocampus")
    evaluator.add_label(4, "Amygdala")
    evaluator.add_label(5, "Thalamus")
    return evaluator
def init_evaluator(write_to_console: bool = True,
                   csv_file: str = None,
                   calculate_distance_metrics: bool = False):
    evaluator = eval.Evaluator(EvaluatorAggregator())
    if write_to_console:
        evaluator.add_writer(eval.ConsoleEvaluatorWriter(5))
    if csv_file is not None:
        evaluator.add_writer(eval.CSVEvaluatorWriter(csv_file))
    if calculate_distance_metrics:
        evaluator.metrics = [
            pymia_metric.DiceCoefficient(),
            pymia_metric.HausdorffDistance(),
            pymia_metric.HausdorffDistance(percentile=95, metric='HDRFDST95'),
            pymia_metric.VolumeSimilarity()
        ]
    else:
        evaluator.metrics = [
            pymia_metric.DiceCoefficient(),
            pymia_metric.VolumeSimilarity()
        ]
    evaluator.add_label(1, cfg.FOREGROUND_NAME)
    return evaluator
def init_evaluator(directory: object, result_file_name: object = 'results.csv') -> object:
    """Initializes an evaluator.

    Args:
        directory (str): The directory for the results file.
        result_file_name (str): The result file name (CSV file).

    Returns:
        eval.Evaluator: An evaluator.
    """
    os.makedirs(directory, exist_ok=True)  # generate result directory, if it does not exists

    evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
    evaluator.add_writer(eval_.CSVEvaluatorWriter(os.path.join(directory, result_file_name)))
    evaluator.add_label(1, 'WhiteMatter')
    evaluator.add_label(2, 'GreyMatter')
    evaluator.add_label(3, 'Hippocampus')
    evaluator.add_label(4, 'Amygdala')
    evaluator.add_label(5, 'Thalamus')
    evaluator.metrics = [metric.DiceCoefficient(), metric.HausdorffDistance()]
    # warnings.warn('Initialized evaluation with the Dice coefficient. Do you know other suitable metrics?')
    # you should add more metrics than just the Hausdorff distance!
    return evaluator
예제 #9
0
def main(inputdir: str, csvoutputdir: str, segname: str):

    # ! THIS PARAMETER FOR THE DEFORMATION SIGMA HAS TO BE TUNED PER LABEL TO MATCH INTERRATER-VARIABILITY ! #
    sigmaarr = np.linspace(2, 8, 31)

    subjroot = '/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/MANAGE/data/robustness/preprocessed_segmented'
    csvoutputdir = os.path.join(
        '/media/yannick/c4a7e8d3-9ac5-463f-b6e6-92e216ae6ac0/MANAGE/data/robustness/segdeform/interrateroutput',
        segname)

    # make output directory if it does not already exists
    if not os.path.isdir(csvoutputdi):
        os.makedirs(csvoutputdi)

    patlist = os.listdir(subjroot)

    evaluator = pymia_eval.Evaluator(pymia_eval.ConsoleEvaluatorWriter(5))
    evaluator.add_label(1, segname)
    evaluator.add_metric(pymia_metric.DiceCoefficient())

    # for sigmaidx, sigma in enumerate(deformation_sigma):
    for sigmaval in sigmaarr:
        evaluator.add_writer(
            pymia_eval.CSVEvaluatorWriter(
                os.path.join(csvoutputdir,
                             'results_' + str(sigmaval) + '.csv')))
        for patidx, pat in enumerate(patlist):
            # read CET image
            img_orig = sitk.ReadImage(
                os.path.join(subjroot, pat, pat + '_' + segname + '.nii.gz'))
            for runidx_cet in range(0, 100):
                deformed = elasticdeform(img_orig, sigmaval)

                evaluator.evaluate(
                    img_orig, deformed,
                    pat + '_' + str(sigmaval) + '_' + str(runidx_cet))
예제 #10
0
파일: main.py 프로젝트: CGPiguet/MyMIALab
def atlas_creation():
    #Load the train labels_native with their transform
    wdpath = 'C:/Users/Admin/PycharmProjects/MyMIALab/data/train'
    results_labels_nii = []
    results_affine = []
    resample_labels = []

    for dirpath, subdirs, files in os.walk(wdpath):
        for x in files:
            if x.endswith("labels_native.nii.gz"):
                results_labels_nii.append(os.path.join(dirpath, x))
            if x.endswith("affine.txt"):
                results_affine.append(os.path.join(dirpath, x))

    #Resample the train labels_native with the transform
    for i in range(0, len(results_affine)):
        transform = sitk.ReadTransform(results_affine[i])
        labels_image = sitk.ReadImage(results_labels_nii[i])
        resample_image = sitk.Resample(labels_image, transform,
                                       sitk.sitkNearestNeighbor, 0,
                                       labels_image.GetPixelIDValue())
        resample_labels.append(resample_image)
        #without resample
        #resample_labels.append(labels_image)

    # Threshold the images to sort them in 5 categories
    white_matter_list = []
    grey_matter_list = []
    hippocampus_list = []
    amygdala_list = []
    thalamus_list = []
    for i in range(0, len(resample_labels)):
        white_matter_list.append(sitk.Threshold(resample_labels[i], 1, 1, 0))
        grey_matter_list.append(sitk.Threshold(resample_labels[i], 2, 2, 0))
        hippocampus_list.append(sitk.Threshold(resample_labels[i], 3, 3, 0))
        amygdala_list.append(sitk.Threshold(resample_labels[i], 4, 4, 0))
        thalamus_list.append(sitk.Threshold(resample_labels[i], 5, 5, 0))

    #sum them up and divide by their number of images to make a probability map
    white_matter_map = 0
    grey_matter_map = 0
    hippocampus_map = 0
    amygdala_map = 0
    thalamus_map = 0

    for i in range(1, len(resample_labels)):
        white_matter_map = sitk.Add(white_matter_map, white_matter_list[i])
        grey_matter_map = sitk.Add(grey_matter_map, grey_matter_list[i])
        hippocampus_map = sitk.Add(hippocampus_map, hippocampus_list[i])
        amygdala_map = sitk.Add(amygdala_map, amygdala_list[i])
        thalamus_map = sitk.Add(thalamus_map, thalamus_list[i])

    white_matter_map = sitk.Divide(white_matter_map, len(white_matter_list))
    grey_matter_map = sitk.Divide(grey_matter_map, len(grey_matter_list))
    hippocampus_map = sitk.Divide(hippocampus_map, len(hippocampus_list))
    amygdala_map = sitk.Divide(amygdala_map, len(amygdala_list))
    thalamus_map = sitk.Divide(thalamus_map, len(thalamus_list))
    #atlas = sitk.Divide(sum_images, len(test_resample))
    #slice = sitk.GetArrayFromImage(atlas)[90,:,:]
    #plt.imshow(slice)

    sitk.WriteImage(
        hippocampus_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/hippocampus_map_no_threshold.nii',
        False)
    sitk.WriteImage(
        white_matter_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/white_matter_map_no_threshold.nii',
        False)
    sitk.WriteImage(
        grey_matter_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/grey_matter_map_no_threshold.nii',
        False)
    sitk.WriteImage(
        amygdala_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/amygdala_map_no_threshold.nii',
        False)
    sitk.WriteImage(
        thalamus_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/thalamus_map_no_threshold.nii',
        False)

    #Threhold the 5 different maps to get a binary map
    white_matter_map = sitk.BinaryThreshold(white_matter_map, 0, 1, 1, 0)
    grey_matter_map = sitk.BinaryThreshold(grey_matter_map, 0, 2, 2, 0)
    hippocampus_map = sitk.BinaryThreshold(hippocampus_map, 0, 3, 3, 0)
    amygdala_map = sitk.BinaryThreshold(amygdala_map, 0, 4, 4, 0)
    thalamus_map = sitk.BinaryThreshold(thalamus_map, 0, 5, 5, 0)

    #Save the images
    sitk.WriteImage(
        grey_matter_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/grey_matter_map.nii',
        False)
    sitk.WriteImage(
        white_matter_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/white_matter_map.nii',
        False)
    sitk.WriteImage(
        hippocampus_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/hippocampus_map.nii',
        False)
    sitk.WriteImage(
        amygdala_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/amygdala_map.nii',
        False)
    sitk.WriteImage(
        thalamus_map,
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/thalamus_map.nii',
        False)

    #Load the test labels_native and their transform
    wdpath_test = 'C:/Users/Admin/PycharmProjects/MyMIALab/data/test'
    test_results_nii = []
    test_results_affine = []
    test_resample = []
    for dirpath, subdirs, files in os.walk(wdpath_test):
        for x in files:
            if x.endswith("labels_native.nii.gz"):
                test_results_nii.append(os.path.join(dirpath, x))
            if x.endswith("affine.txt"):
                test_results_affine.append(os.path.join(dirpath, x))

    #Resample the labels_native with the transform
    for i in range(0, len(test_results_affine)):
        test_transform = sitk.ReadTransform(test_results_affine[i])
        test_image = sitk.ReadImage(test_results_nii[i])
        test_resample_image = sitk.Resample(test_image, test_transform,
                                            sitk.sitkNearestNeighbor)
        test_resample.append(test_resample_image)
        #Without resample
        #test_resample.append(test_image)

    #Save the first test patient labels
    sitk.WriteImage(
        test_resample[0],
        'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result/test.nii',
        False)

    #Compute the dice coeefficent (and the Hausdorff distance)
    label_list = [
        'White Matter', 'Grey Matter', 'Hippocampus', 'Amygdala', 'Thalamus'
    ]
    map_list = [
        white_matter_map, grey_matter_map, hippocampus_map, amygdala_map,
        thalamus_map
    ]
    dice_list = []
    for i in range(0, 5):
        evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
        evaluator.metrics = [
            metric.DiceCoefficient(),
            metric.Sensitivity(),
            metric.Precision(),
            metric.Fallout()
        ]
        evaluator.add_writer(
            eval_.CSVEvaluatorWriter(
                os.path.join(
                    'C:/Users/Admin/PycharmProjects/MyMIALab/bin/mia-result',
                    'Results_' + label_list[i] + '.csv')))
        evaluator.add_label(i + 1, label_list[i])
        for j in range(0, len(test_resample)):
            evaluator.evaluate(test_resample[j], map_list[i],
                               'Patient ' + str(j))
예제 #11
0
def load_atlas_custom_images(wdpath):
    # params_list = list(data_batch.items())
    # print(params_list[0] )
    t1w_list = []
    t2w_list = []
    gt_label_list = []
    brain_mask_list = []
    transform_list = []

    #Load the train labels_native with their transform
    for dirpath, subdirs, files in os.walk(wdpath):
        # print("dirpath", dirpath)
        # print("subdirs", subdirs)
        # print("files", files)
        for x in files:
            if x.endswith("T1native.nii.gz"):
                t1w_list.append(sitk.ReadImage(os.path.join(dirpath, x)))
            elif x.endswith("T2native.nii.gz"):
                t2w_list.append(sitk.ReadImage(os.path.join(dirpath, x)))
            elif x.endswith("labels_native.nii.gz"):
                gt_label_list.append(sitk.ReadImage(os.path.join(dirpath, x)))
            elif x.endswith("Brainmasknative.nii.gz"):
                brain_mask_list.append(sitk.ReadImage(os.path.join(dirpath,
                                                                   x)))
            elif x.endswith("affine.txt"):
                transform_list.append(
                    sitk.ReadTransform(os.path.join(dirpath, x)))
            # else:
            #     print("Problem in CustomAtlas in folder", dirpath)

    #Resample and thershold to get the label
    white_matter_list = []
    grey_matter_list = []
    hippocampus_list = []
    amygdala_list = []
    thalamus_list = []
    for i in range(0, len(gt_label_list)):
        resample_img = sitk.Resample(gt_label_list[i], atlas_t1,
                                     transform_list[i],
                                     sitk.sitkNearestNeighbor, 0,
                                     gt_label_list[i].GetPixelIDValue())
        white_matter_list.append(sitk.Threshold(resample_img, 1, 1, 0))
        grey_matter_list.append(sitk.Threshold(resample_img, 2, 2, 0))
        hippocampus_list.append(sitk.Threshold(resample_img, 3, 3, 0))
        amygdala_list.append(sitk.Threshold(resample_img, 4, 4, 0))
        thalamus_list.append(sitk.Threshold(resample_img, 5, 5, 0))

    #Save each label from first data
    path_to_save = '../bin/custom_atlas_result/'
    if not os.path.exists(path_to_save):
        os.makedirs(path_to_save)
    sitk.WriteImage(hippocampus_list[0],
                    os.path.join(path_to_save, 'Hippocampus_label.nii'), True)
    sitk.WriteImage(white_matter_list[0],
                    os.path.join(path_to_save, 'White_matter_label.nii'), True)
    sitk.WriteImage(grey_matter_list[0],
                    os.path.join(path_to_save, 'Grey_matter_label.nii'), True)
    sitk.WriteImage(amygdala_list[0],
                    os.path.join(path_to_save, 'Amygdala_label.nii'), True)
    sitk.WriteImage(thalamus_list[0],
                    os.path.join(path_to_save, 'Thalamus_label.nii'), True)

    #Save an image resampled to show segmentation
    sitk.WriteImage(gt_label_list[0],
                    os.path.join(path_to_save, 'Train_image_1_resampled.nii'),
                    True)

    # sum them up and divide by their number of images to make a probability map
    white_matter_map = 0
    grey_matter_map = 0
    hippocampus_map = 0
    amygdala_map = 0
    thalamus_map = 0
    for i in range(1, len(gt_label_list)):
        white_matter_map = sitk.Add(white_matter_map, white_matter_list[i])
        grey_matter_map = sitk.Add(grey_matter_map, grey_matter_list[i])
        hippocampus_map = sitk.Add(hippocampus_map, hippocampus_list[i])
        amygdala_map = sitk.Add(amygdala_map, amygdala_list[i])
        thalamus_map = sitk.Add(thalamus_map, thalamus_list[i])

    white_matter_map = sitk.Divide(white_matter_map, len(white_matter_list))
    grey_matter_map = sitk.Divide(grey_matter_map, len(grey_matter_list))
    hippocampus_map = sitk.Divide(hippocampus_map, len(hippocampus_list))
    amygdala_map = sitk.Divide(amygdala_map, len(amygdala_list))
    thalamus_map = sitk.Divide(thalamus_map, len(thalamus_list))
    #atlas = sitk.Divide(sum_images, len(test_resample))
    #slice = sitk.GetArrayFromImage(atlas)[90,:,:]
    #plt.imshow(slice)

    #Register without threshold
    path_to_save = '../bin/custom_atlas_result/'
    if not os.path.exists(path_to_save):
        os.makedirs(path_to_save)
    sitk.WriteImage(
        grey_matter_map,
        os.path.join(path_to_save, 'grey_matter_map_no_threshold.nii'), True)
    sitk.WriteImage(
        white_matter_map,
        os.path.join(path_to_save, 'white_matter_map_no_threshold.nii'), True)
    sitk.WriteImage(
        hippocampus_map,
        os.path.join(path_to_save, 'hippocampus_map_no_threshold.nii'), True)
    sitk.WriteImage(
        amygdala_map,
        os.path.join(path_to_save, 'amygdala_map_no_threshold.nii'), True)
    sitk.WriteImage(
        thalamus_map,
        os.path.join(path_to_save, 'thalamus_map_no_threshold.nii'), True)

    #Threhold the 5 different maps to get a binary map
    white_matter_map = sitk.BinaryThreshold(white_matter_map, 0.3, 1, 1, 0)
    grey_matter_map = sitk.BinaryThreshold(grey_matter_map, 0.6, 2, 2, 0)
    hippocampus_map = sitk.BinaryThreshold(hippocampus_map, 0.9, 3, 3, 0)
    amygdala_map = sitk.BinaryThreshold(amygdala_map, 1.2, 4, 4, 0)
    thalamus_map = sitk.BinaryThreshold(thalamus_map, 1.5, 5, 5, 0)

    #Save the images
    path_to_save = '../bin/custom_atlas_result/'
    if not os.path.exists(path_to_save):
        os.makedirs(path_to_save)
    sitk.WriteImage(grey_matter_map,
                    os.path.join(path_to_save, 'grey_matter_map.nii'), True)
    sitk.WriteImage(white_matter_map,
                    os.path.join(path_to_save, 'white_matter_map.nii'), True)
    sitk.WriteImage(hippocampus_map,
                    os.path.join(path_to_save, 'hippocampus_map.nii'), True)
    sitk.WriteImage(amygdala_map, os.path.join(path_to_save,
                                               'amygdala_map.nii'), True)
    sitk.WriteImage(thalamus_map, os.path.join(path_to_save,
                                               'thalamus_map.nii'), True)

    # Load the test labels_native and their transform
    path_to_test = '../data/test'
    test_gt_label_list = []
    test_transform_list = []

    for dirpath, subdirs, files in os.walk(path_to_test):
        for x in files:
            if x.endswith("labels_native.nii.gz"):
                test_gt_label_list.append(
                    sitk.ReadImage(os.path.join(dirpath, x)))
            if x.endswith("affine.txt"):
                test_transform_list.append(
                    sitk.ReadTransform(os.path.join(dirpath, x)))

    #Resample the labels_native with the transform
    test_resample_img = []
    for i in range(0, len(test_gt_label_list)):
        resample_img = sitk.Resample(test_gt_label_list[i], atlas_t1,
                                     test_transform_list[i],
                                     sitk.sitkNearestNeighbor, 0,
                                     test_gt_label_list[i].GetPixelIDValue())

        test_resample_img.append(resample_img)

    sitk.WriteImage(test_resample_img[0],
                    os.path.join(path_to_save, 'Test_data_1_resampled.nii'),
                    True)

    # Save the first test patient labels
    # path_to_save = '../bin/temp_test_result/'
    # if not os.path.exists(path_to_save):
    #     os.makedirs(path_to_save)
    # sitk.WriteImage(test_resample_img[0], os.path.join(path_to_save, 'FirstPatienFromTestList.nii'), False)

    #Compute the dice coeefficent (and the Hausdorff distance)
    label_list = [
        'White Matter', 'Grey Matter', 'Hippocampus', 'Amygdala', 'Thalamus'
    ]
    map_list = [
        white_matter_map, grey_matter_map, hippocampus_map, amygdala_map,
        thalamus_map
    ]
    dice_list = []

    path_to_save = '../bin/DiceTestResult/'
    if not os.path.exists(path_to_save):
        os.makedirs(path_to_save)
    for i in range(0, 5):
        evaluator = eval_.Evaluator(eval_.ConsoleEvaluatorWriter(5))
        evaluator.metrics = [
            metric.DiceCoefficient(),
            metric.HausdorffDistance()
        ]
        evaluator.add_writer(
            eval_.CSVEvaluatorWriter(
                os.path.join(path_to_save,
                             'DiceResults_' + label_list[i] + '.csv')))
        evaluator.add_label(i + 1, label_list[i])
        for j in range(0, len(test_resample_img)):
            evaluator.evaluate(test_resample_img[j], map_list[i],
                               'Patient ' + str(j))

    print("END Custom loadAtlas")