Cannot perform evaluation over PROMISE12 dataset

[mariamhrr]

Hello, I have trained my segmentation network over the PROMISE12 dataset using the dense_vnet, but when I try to perform evaluation between the PROMISE12 groundtruth and my inferred volumes there appears this error:

File "C:\Users\NiftyNet\niftynet\evaluation\segmentation_evaluations.py", line 160, in metric_from_binarized conf_mat = np.array([[np.sum(land(lnot(seg), lnot(ref))), ValueError: operands could not be broadcast together with shapes (320,320,20,1,1) (512,512,54,1,1)

I understand that its because the PROMISE12 files have (512,512,54,1,1) dimension and my inferred volumes have (320,320,20,1,1) dimension. Shouldn't it be doing resize automatically? Also, it is strange to me that the inferred volumes have (512,512,54,1,1) dimension when I specified them to be (64,64,64,1,1) on my config.ini file. Thanks for the help!

Ps. here is my config file:

[seg] path_to_search = C:/Users/INFERENCE/5000 filename_contains = _niftynet_out filename_not_contains = spatial_window_size = (64,64,64) axcodes=(S,L,P) interp_order = 3 [label] path_to_search = C:/Users/Promise12_LABELS filename_contains = Case, _segmentation filename_not_contains = spatial_window_size = (64,64,64) axcodes=(S,L,P) interp_order = 0

[SYSTEM] cuda_devices = "" num_threads = 2 num_gpus = 1 model_dir = C:/Users/models

[NETWORK] name = vnet activation_function = prelu batch_size = 1 volume_padding_size = 0 histogram_ref_file = ../standardisation_models.txt norm_type = percentile cutoff = (0.01, 0.99) normalisation = True whitening = True normalise_foreground_only=True foreground_type = otsu_plus multimod_foreground_type = and window_sampling = resize queue_length = 8

[INFERENCE] save_seg_dir = ./INFERENCE/5000 output_interp_order = 3 spatial_window_size = (64,64,64) inference_iter = 5000

[EVALUATION] save_csv_dir = C:/Users/models evaluations = dice,jaccard

[SEGMENTATION] inferred = seg label = label output_prob = False num_classes = 2 label_normalisation = True

[shanpriya3]

I am facing the same issue with 3D Unet for two class segmentation. Could someone help? Thanks!

[shanpriya3]

Here is my configuration file:

############################ input configuration sections [ct] path_to_search = ./data/ct/ filename_contains = CT spatial_window_size = (96, 96, 96) pixdim = (1.0, 1.0, 1.0) interp_order = 3 axcodes=(A, R, S)

[seg] csv_file = ./models/unetH/segmentation_output/inference/inferred.csv path_to_search = ./models/unetH/segmentation_output/inference filename_contains = niftynet spatial_window_size = (96, 96, 96) interp_order = 0

[label] path_to_search = ./data/ct/ filename_contains = Label spatial_window_size = (96, 96, 96) pixdim = (1.0, 1.0, 1.0) interp_order = 0 axcodes=(A, R, S)

############################## system configuration sections [SYSTEM] cuda_devices = "" num_threads = 2 num_gpus = 2 model_dir = models/unetH queue_length = 36

[NETWORK] name = unet activation_function = prelu batch_size = 1 decay = 0 reg_type = L2

volume level preprocessing

volume_padding_size = 44 histogram_ref_file = ./models/unetH/histogram_ref_file.txt norm_type = percentile cutoff = (0.01, 0.99) normalisation=True whitening = True normalise_foreground_only=True foreground_type = otsu_plus window_sampling = uniform

[TRAINING] sample_per_volume = 32 rotation_angle = (-10.0, 10.0) scaling_percentage = (-10.0, 10.0) random_flipping_axes=0,1 lr = 0.001 loss_type = Dice starting_iter = 0 save_every_n = 500 max_iter = 1000 validation_every_n = 10 validation_max_iter = 5 exclude_fraction_for_validation = 0.2 exclude_fraction_for_inference = 0.1

[INFERENCE] border = (44, 44, 44) inference_iter = 1000 output_interp_order = 0 spatial_window_size = (96, 96, 96) save_seg_dir = ./segmentation_output/inference dataset_to_infer = inference

[EVALUATION] save_csv_dir = ./data/evaluation evaluations = dice,jaccard,false_positive_rate,positive_predictive_values,n_pos_ref,n_pos_seg evaluation_units = foreground

############################ custom configuration sections [SEGMENTATION] image = ct inferred = seg label = label label_normalisation = True output_prob = False num_classes = 2

Here is the error:

File "/anaconda3/envs/NiftyNet/lib/python3.6/site-packages/niftynet/evaluation/segmentation_evaluations.py", line 160, in metric_from_binarized conf_mat = np.array([[np.sum(land(lnot(seg), lnot(ref))), ValueError: operands could not be broadcast together with shapes (600,600,138,1,1) (546,546,338,1,1)

[shanpriya3]

Hello, Any update on this issue?