Пример #1
0
def scorer(pred,label,vxlspacing):
	"""

	:param pred:
	:param label:
	:param voxelspacing:
	:return:
	"""

	volscores = {}

	volscores['dice'] = metric.dc(pred,label)
	volscores['jaccard'] = metric.binary.jc(pred,label)
	volscores['voe'] = 1. - volscores['jaccard']
	volscores['rvd'] = metric.ravd(label,pred)

	if np.count_nonzero(pred) ==0 or np.count_nonzero(label)==0:
		volscores['assd'] = 0
		volscores['msd'] = 0
	else:
		evalsurf = Surface(pred,label,physical_voxel_spacing = vxlspacing,mask_offset = [0.,0.,0.], reference_offset = [0.,0.,0.])
		volscores['assd'] = evalsurf.get_average_symmetric_surface_distance()
	
		volscores['msd'] = metric.hd(label,pred,voxelspacing=vxlspacing)



	logging.info("\tDice " + str(volscores['dice']))
	logging.info("\tJaccard " + str(volscores['jaccard']))
	logging.info("\tVOE " + str(volscores['voe']))
	logging.info("\tRVD " + str(volscores['rvd']))
	logging.info("\tASSD " + str(volscores['assd']))
	logging.info("\tMSD " + str(volscores['msd']))

	return volscores
Пример #2
0
def get_assd(preds_path, labels_path, dicoms_path, result_path, ):
    test_time = time.strftime("%Y.%m.%d.%H.%M.%S", time.localtime())
    result = os.path.join(result_path, "results.txt")
    assd = {}
    all_assd = 0
    # 获取病人姓名
    patients = os.listdir(preds_path)
    for patient in patients:
        # 获取标签集合和预测集合
        pred_path = os.path.join(preds_path, patient)
        label_path = os.path.join(labels_path, patient)
        dicom_path = os.path.join(dicoms_path, patient)
        preds = sorted(glob.glob(os.path.join(pred_path, "*.bmp")))
        labels = sorted(glob.glob(os.path.join(label_path, "*.bmp")))
        # 创建array变量,存储每个病人图片数据
        [width, height] = imageio.imread(preds[0]).shape
        pred_rawData = np.zeros((width, height, len(preds)))
        label_rawData = np.zeros((width, height, len(labels)))
        i = 0
        for pred, label in zip(preds, labels):
            # 读取图片,获取numpy array
            pred_array = imageio.imread(pred)
            label_array = imageio.imread(label)
            # 进行二值化处理
            pred_rawData[:, :, i] = pred_array / 255
            label_rawData[:, :, i] = label_array / 255
            # 循环将每张图片数据进行转换并保存
            i += 1
        # 对array进行数据格式转换
        pred_rawData = pred_rawData.astype(int)
        label_rawData = label_rawData.astype(int)
        # print("*"*30)
        print(dicom_path)
        pixel_info = get_pixel_info(dicom_path)
        print(patient, "_像素间距:", pixel_info)
        # print("*"*30)
        evalsurf = Surface(pred_rawData, label_rawData, physical_voxel_spacing=pixel_info, mask_offset=[0., 0., 0.],
                           reference_offset=[0., 0., 0.])
        assd[patient] = evalsurf.get_average_symmetric_surface_distance()
        per_result = "'%s'_the average symmetric surface distance:%.2f" % (patient, assd[patient])
        print(per_result)
        with open(result, mode='a') as file:
            file.write(test_time)
            file.write("    ")
            file.write(per_result)
            file.write("\n")

    for key in assd.keys():
        all_assd += assd.get(key)

    average_assd = all_assd / len(assd)
    average_result = "AllPatients_the average symmetric surface distance:%.2f" % (average_assd)
    print(average_result)
    with open(result, mode='a') as file:
        file.write(test_time)
        file.write("    ")
        file.write(average_result)
        file.write("\n")
    return  assd
def compute_segmentation_scores(prediction_mask, reference_mask,
                                voxel_spacing):
    """
    Calculates metrics scores from numpy arrays and returns an dict.
    Assumes that each object in the input mask has an integer label that
    defines object correspondence between prediction_mask and
    reference_mask.
    :param prediction_mask: numpy.array, int
    :param reference_mask: numpy.array, int
    :param voxel_spacing: list with x,y and z spacing
    :return: dict with dice, jaccard, voe, rvd, assd, rmsd, and msd
    """
    scores = {
        'dice': [],
        'jaccard': [],
        'voe': [],
        'rvd': [],
        'assd': [],
        'rmsd': [],
        'msd': []
    }

    p = (prediction_mask > 0)
    r = (reference_mask > 0)
    if np.any(p) and np.any(r):
        dice = metric.dc(p, r)
        jaccard = dice / (2. - dice)
        scores['dice'].append(dice)
        scores['jaccard'].append(jaccard)
        scores['voe'].append(1. - jaccard)
        scores['rvd'].append(metric.ravd(r, p))
        evalsurf = Surface(p,
                           r,
                           physical_voxel_spacing=voxel_spacing,
                           mask_offset=[0., 0., 0.],
                           reference_offset=[0., 0., 0.])

        assd = evalsurf.get_average_symmetric_surface_distance()
        rmsd = evalsurf.get_root_mean_square_symmetric_surface_distance()
        msd = evalsurf.get_maximum_symmetric_surface_distance()
        scores['assd'].append(assd)
        scores['rmsd'].append(rmsd)
        scores['msd'].append(msd)
    else:
        # There are no objects in the prediction, in the reference, or both
        scores['dice'].append(0)
        scores['jaccard'].append(0)
        scores['voe'].append(1.)
        # Surface distance (and volume difference) metrics between the two
        # masks are meaningless when any one of the masks is empty. Assign
        # maximum penalty. The average score for these metrics, over all
        # objects, will thus also not be finite as it also loses meaning.
        scores['rvd'].append(LARGE)
        scores['assd'].append(LARGE)
        scores['rmsd'].append(LARGE)
        scores['msd'].append(LARGE)

    return scores
Пример #4
0
def get_scores(pred,label,vxlspacing):
	volscores = {}

	volscores['dice'] = metric.dc(pred,label)
	volscores['jaccard'] = metric.binary.jc(pred,label)
	volscores['voe'] = 1. - volscores['jaccard']
	volscores['rvd'] = metric.ravd(label,pred)

	if np.count_nonzero(pred) ==0 or np.count_nonzero(label)==0:
		volscores['assd'] = 0
		volscores['msd'] = 0
	else:
		evalsurf = Surface(pred,label,physical_voxel_spacing = vxlspacing,mask_offset = [0.,0.,0.], reference_offset = [0.,0.,0.])
		volscores['assd'] = evalsurf.get_average_symmetric_surface_distance()

		volscores['msd'] = metric.hd(label,pred,voxelspacing=vxlspacing)

	return volscores