This repository provides the official implementation of CROPro: A tool for automated cropping of prostate MR images.
DOI: https://doi.org/10.1117/1.JMI.10.2.024004
Purpose: To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images.
Approach: CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.g., prostate) with different image sizes, pixel spacing, and sampling strategies. Performance was evaluated in the context of clinically significant prostate cancer (csPCa) classification. Transfer learning was used to train five convolutional neural network (CNN) and five vision transformer (ViT) models using different combinations of cropped image sizes (64 × 64, 128 × 128, and 256 × 256 pixels2), pixel spacing (0.2 × 0.2, 0.3 × 0.3, 0.4 × 0.4, and 0.5 × 0.5 mm2 ), and sampling strategies (center, random, and stride cropping) over the prostate. T2-weighted MR images (N = 1475) from the online available PI-CAI challenge were used to train (N = 1033), validate (N = 221), and test (N = 221) all models.
Results: Among CNNs, SqueezeNet with stride cropping (image size: 128 × 128, pixel spacing: 0.2 × 0.2 mm2 ) achieved the best classification performance (0.678 ± 0.006). Among ViTs, ViT-H/14 with random cropping (image size: 64 × 64 and pixel spacing: 0.5 × 0.5 mm2 ) achieved the best performance (0.756 ± 0.009). Model performance depended on the cropped area, with optimal size generally larger with center cropping (*∼40 cm2 *) than random/stride cropping (∼10 cm2 ).
Conclusion: We found that csPCa classification performance of CNNs and ViTs depends on the cropping settings. We demonstrated that CROPro is well suited to optimize these settings in a standardized manner, which could improve the overall performance of deep learning models.
Note: The official implementation was performed with T2-weighted images. However, in this repository we also implemented cropping biparametric MRI (bpMRI) sequences such as T2-Weigted (T2W), Apparent diffusion coefficient (ADC) and high-b-value (HBV) Diffusion-weighted (DWI) MRI.
First, there are three different techniques implemented to crop images: Center, Random and Stride cropping. Second, depending on patient status (negative/unknown and positive), CROPro can be used to crop patches of MRI images with different settings.
Below, we provide an example:
- In a patient with negative ("healthy") or a patient with ("unknown") health status, CROPro can be used to crop image patches over the prostate area. In both scenarios (negative/unknown), segmentation of the MRI prostate gland area is required.
- In a patient with positive health status ("lesion"), CROPro can be used to crop areas of MRI images with lesions. This setting requires masking/segmentation of both the prostate and the lesion.
- There are also a number of settings that can be used. These include
automatic normalization,exclude slicesfrom the apex (first) and base (last) of MRI images,lesions and prostate gland segmentation overlap criteria(e.g, crop if lesion and prostate segmentation overlap on a % basis), more on this inmain.py.- In addition, for
positivepatients, the method uses prostate segmentation to crop the images. However, for each cropped image, a comparison of the lesion area within the cropped image is automatically performed. If this is successful, the cropped images are saved.- Good to know! When cropping with CROPro for a
negativepatient, CROPro uses the slices for which there is prostate gland segmentation. Further, it will exclude slices depending on the size of the segmentation (e.g., very small segmentation areas are not considered).
- In this implementation, the main reasons of cropping slices with lesions were to use them for training the AI model and to test image-level classification (e.g., image-level AUC).
- In a scenario where the patient's health status (negative="healthy" or positive=malignant) is not given. In a test case scenario, i.e. a scenario where the patient’s health status is “unknown”, the goal would be to crop and test all slices of a patient for which segmentations are available (e.g., AI or human segmentation)
since some of the slices may have lesions. - In the case of a negative patient, the prostate segmentation mask is used. If no information is available about the patient, i.e., in the case of a patient with unknown health status, prostate segmentation is also used to crop all slices. This means that prostate segmentation is used for both negative and unknown patient's health status. Although both have the same function, in a test case scenario the patient's health status is unknown. Therefore, it is important to separate the two so that the 'unknown' patient status can be distinguished from the 'negative' patient status.
Examples
Negative: a patient with non-significant prostate cancer (Gleason Grade Group <= 1)
Positive : a patient with clinically significant prostate cancer (Gleason Grade Group => 2)
Negative
The blue area represents prostate gland segmentation.
MRI Images: T2w (left) - ADC (middle) - HBV (right)
Method: Stride Cropping
Positive
The red area represents lesion segmentations
Method: Stride Cropping
TW2 (left) - ADC (middle) - HBV (right)
$ git clone https://github.com/alexofficial/CROPro.git
$ conda env create -f conda_env/cropro.yml$ conda activate cropro
Note: if you encounter any error you can manually download the necessary packages: see
requirements.txt
To familiarize yourself with CROPro, we recommend that you take a look at the examples before starting your own data set.
We have provided two patients from PI-CAI challenge and PI-CAI Study Protocol. The two selected patients:
Negative: 10001_1000001Positive: 10117_1000117
Selection (above) is based on clinically significant prostate cancer (csPCa). Prostate gland masks (segmentation) and lesion masks are in the dataset folder.
- Positive Patient:
10117_1000117from PI-CAI challenge dataset. We have provided:- T2W image (
10117_1000117_T2WI.nii.gz) - Normalized T2W image (
10117_1000117_NormT2WI.nii.gz), - Co-registered ADC (
10117_1000117_ADC.nii.gz) - Co-registered HBV (
10117_1000117_HBV.nii.gz) - Prostate masks (
10117_1000117_ProstateMask.nii.gz/AI-gland/10117_1000117.nii.gz) - Lesions masks (
AI_labels/10117_1000117.nii.gzorhuman_labels/10117_1000117.nii.gz)
- T2W image (
- Negative patient:
10001_1000001from PI-CAI challenge dataset. We have provided:- T2W image (
10001_1000001_T2WI.nii.gz) - Normalized T2W image (
10001_1000001_NormT2WI.nii.gz), - Co-registered ADC (
10001_1000001_ADC.nii.gz) - Co-registered HBV (
10001_1000001_HBV.nii.gz) - Prostate masks (
10001_1000001_ProstateMask.nii.gz)
- T2W image (
$ bash download_dataset.shordownload_dataset.ps1
if none works then you can nagivate to this link, manually download and unzip and then move the dataset folder insider CROPro folder.
📦dataset
┗ 📂PI-CAI
┃ ┣ 📂negative
┃ ┃ ┗ 📂10001_1000001
┃ ┃ ┃ ┣ 📜10001_1000001_ADC.nii.gz
┃ ┃ ┃ ┣ 📜10001_1000001_HBV.nii.gz
┃ ┃ ┃ ┣ 📜10001_1000001_NormT2WI.nii.gz
┃ ┃ ┃ ┣ 📜10001_1000001_ProstateMask.nii.gz
┃ ┃ ┃ ┗ 📜10001_1000001_T2WI.nii.gz
┃ ┣ 📂positive
┃ ┃ ┗ 📂10117_1000117
┃ ┃ ┃ ┣ 📜10117_1000117_ADC.nii.gz
┃ ┃ ┃ ┣ 📜10117_1000117_HBV.nii.gz
┃ ┃ ┃ ┣ 📜10117_1000117_NormT2WI.nii.gz
┃ ┃ ┃ ┣ 📜10117_1000117_ProstateMask.nii.gz
┃ ┃ ┃ ┗ 📜10117_1000117_T2WI.nii.gz
┃ ┗ 📂segmentation
┃ ┃ ┣ 📂AI-gland
┃ ┃ ┃ ┗ 📜10117_1000117.nii.gz
┃ ┃ ┣ 📂AI_labels
┃ ┃ ┃ ┗ 📜10117_1000117.nii.gz
┃ ┃ ┗ 📂human_labels
┃ ┃ ┃ ┗ 📜10117_1000117.nii.gz
You can run all examples using: $ bash examples.sh
Cropping examples includes:
$ python PI-CAI_positive_crop.py$ python PI-CAI_negative_crop.py
To run CROPro, you need a DICOM file in (.mhd, .mhd, nii.gz), a prostate gland segmentation file (.mhd, .mhd, nii.gz). Also, if the patient has a positive health status, you will also need a lesion segmentation (.mhd, .mhd, nii.gz).
Code for Negative caseYou can import the class and run CROPro directly from a python file. For example, to run for a negative case with stride cropping technique:
# Negative CASE
import os
from main import CROPro
####### CROPRO settings #######
sequence_type = 'bpMRI' # bpMRI or T2W
crop_method = 'stride' # Crop Method, here we can choose between "stride", "random" and "center".
patient_status = 'negative' # Patients health status, here we can choose between "negative", "positive" and "unknown".
pixel_spacing = 0.4 # Resample the original image to a specific pixel spacing.
crop_image_size = 128 # Crop image patches of different sizes, 64x64, 128x128, 256x256, and so on.
crop_stride = 32 # The crop stride number is a factor when using the stride-crop technique, which allows you to stride over the prostate gland.
sample_number = 12 # The sample number is a factor when using the random-crop technique
normalized_image = True # In case the original images were normalized, we need to define normalized_image equal to True
do_normalization = True # if this is false, normalized_image must be false
# when you perform normalization, the images are normalized with min = 0% and max = 95% percentile of the current sequence and slice.
# The original implementation uses only normalized T2W images, which means that do_normalization=False. (See main.py)
# The file responsible for saving is located at class- > saveFilesC.py
if do_normalization:
normalized_image=False
# In case you want to exclude slices. For example, the first (APEX) and the last (BASE) slice you need to set keep_all_slice = False
# and number_of_slices_to_exclude_from_mask_gland = [1,2,..,N], which will remove the first and the last slice found with segmentation of the prostate gland.
keep_all_slice = True
number_of_slices_to_exclude_from_mask_gland = 1
saved_image_type = "png" # choose your desireble format for the croped patches to be saved
####### PATHS #######
orig_img_path_t2w = 'dataset/PI-CAI/negative/10001_1000001/10001_1000001_NormT2WI.nii.gz' # path to the original T2w image
orig_img_path_adc = 'dataset/PI-CAI/negative/10001_1000001/10001_1000001_ADC.nii.gz'# path to the original ADC image
orig_img_path_hbv = 'dataset/PI-CAI/negative/10001_1000001/10001_1000001_HBV.nii.gz'# path to the original HBV image
seg_img_path_gland = 'dataset/PI-CAI/negative/10001_1000001/10001_1000001_ProstateMask.nii.gz' # Prostate segmentation MASK
patient_case_id = orig_img_path_t2w.rsplit('/')[3]
path_to_save = os.path.join(os.getcwd(), 'dataset', 'cropro','PICAI', 'PICAI_'+ str(crop_method) +'_'+ str(pixel_spacing) +'_'+ str(crop_image_size) \
+'_'+ str(patient_status), str(patient_case_id) )# path to be saved
####### CROPRO class #######
CROProC = CROPro(crop_method=crop_method, orig_img_path_t2w=orig_img_path_t2w,orig_img_path_adc=orig_img_path_adc,orig_img_path_hbv=orig_img_path_hbv,
seg_img_path=seg_img_path_gland, patient_status=patient_status,
sequence_type=sequence_type,
pixel_spacing=pixel_spacing, crop_image_size=crop_image_size,
sample_number=sample_number, normalized_image=normalized_image,
do_normalization=do_normalization, saved_image_type=saved_image_type,
path_to_save=path_to_save, keep_all_slice=keep_all_slice,
number_of_slices_to_exclude_from_mask_gland=number_of_slices_to_exclude_from_mask_gland)
CROProC.cropro()You can run using: $ python PI-CAI_negative_crop.py.
# POSITIVE CASE
import os
from main import CROPro
####### CROPRO settings #######
sequence_type = 'bpMRI' # bpMRI or T2W
crop_method = 'stride' # Crop Method, here we can choose between "stride", "random" and "center".
patient_status = 'positive' # Patients health status, here we can choose between "negative", "positive" and "unknown".
pixel_spacing = 0.5 # Resample the original image to a specific pixel spacing.
crop_image_size = 128 # Crop image patches of different sizes, 64x64, 128x128, 256x256, and so on.
crop_stride = 32 # The crop stride number is a factor when using the stride-crop technique, which allows you to stride over the prostate gland.
normalized_image = True # The original implementation uses only normalized T2W images (using AutoRef), which means that do_normalization=False and save the images using normalized_vmaxNumber = 242 (See main.py)
normalized_vmaxNumber = 242 # if normalized_image is true and the correct range to be saved.
sample_number = 12 # The sample number is a factor when using the random-crop technique
c_min_positive = 0.2 # This factor controls the minimum accepted area of lesion within the cropped image.
# The level of the label. For instance, if both segmentation of the prostate gland (Level=1) and lesion co-exist (Level=2), then tumor_label_level=2.
# However, for PI-CAI dataset that is seperate file. Therefore, tumor_label_level=1.
tumor_label_level = 1
# If you want to normalize the images we provide a normalization using min = 0% and max = 95% percentile of the current sequence and slice.
# These parameters can be change (see main.py)
# the file responsible for saving is located at class- > saveFilesC.py
do_normalization = True # if this is false, normalized_image must be false
if do_normalization:
normalized_image=False
# In case you want to exclude slices. For example, the first (APEX) and the last (BASE) slice you need to set keep_all_slice = False
# and number_of_slices_to_exclude_from_mask_gland = 1, which will remove the first and the last slice found with segmentation of the prostate gland.
keep_all_slice = True
number_of_slices_to_exclude_from_mask_gland = 1
saved_image_type = "png" # choose your desireble format for the croped patches to be saved
####### PATHS #######
orig_img_path_t2w = 'dataset/PI-CAI/positive/10117_1000117/10117_1000117_NormT2WI.nii.gz'
orig_img_path_adc = 'dataset/PI-CAI/positive/10117_1000117/10117_1000117_ADC.nii.gz'
orig_img_path_hbv = 'dataset/PI-CAI/positive/10117_1000117/10117_1000117_HBV.nii.gz'
seg_img_path_gland = 'dataset/PI-CAI/segmentation/AI-gland/10117_1000117.nii.gz' # path to the segmentation image - AI labels
seg_img_path_lesion = 'dataset/PI-CAI/segmentation/AI_labels/10117_1000117.nii.gz' # path to the segmentation image - AI labels
# seg_img_path = 'dataset/PI-CAI/segmentation/human_labels/10117_1000117.nii.gz' # path to the segmentation image - Human labels
patient_case_id = orig_img_path_t2w.rsplit('/')[3]
path_to_save = os.path.join(os.getcwd(), 'dataset', 'cropro','PICAI', 'PICAI_'+ str(crop_method) \
+'_'+ str(pixel_spacing) +'_'+ str(crop_image_size) +'_'+ str(patient_status), str(patient_case_id) ) # path to be saved
####### CROPRO class #######
CROProC = CROPro(crop_method=crop_method, orig_img_path_t2w=orig_img_path_t2w,orig_img_path_adc=orig_img_path_adc,orig_img_path_hbv=orig_img_path_hbv,
seg_img_path=seg_img_path_gland,seg_img_path_lesion=seg_img_path_lesion, patient_status=patient_status, crop_stride=crop_stride,
sequence_type=sequence_type, tumor_label_level=tumor_label_level,
pixel_spacing=pixel_spacing, crop_image_size=crop_image_size,
sample_number=sample_number, normalized_image=normalized_image,
normalized_vmaxNumber=normalized_vmaxNumber, saved_image_type=saved_image_type,
path_to_save=path_to_save, c_min_positive=c_min_positive)
CROProC.cropro()You can run using: $ python PI-CAI_positive_crop.py.
python main.py --crop_method 'stride' --orig_img_path_t2w 'dataset/PI-CAI/negative/10001_1000001/10001_1000001_NormT2WI.nii.gz' --seg_img_path 'dataset/PI-CAI/negative/10001_1000001/10001_1000001_ProstateMask.nii.gz' --tumor_label_level 1 --patient_status negative --pixel_spacing 0.4 --crop_image_size 128 --sequence_type 'T2W' --path_to_save 'dataset/cropro/save_crop/stride_negative_0.4_128'
python main.py --crop_method 'stride' --orig_img_path_t2w 'dataset/PI-CAI/positive/10117_1000117/10117_1000117_NormT2WI.nii.gz' --seg_img_path 'dataset/PI-CAI/segmentation/AI-gland/10117_1000117.nii.gz' --seg_img_path_lesion 'dataset/PI-CAI/segmentation/AI_labels/10117_1000117.nii.gz' --tumor_label_level 1 --patient_status positive --pixel_spacing 0.4 --crop_image_size 128 --sequence_type 'T2W' --path_to_save 'dataset/cropro/save_crop/stride_positive_0.4_128'
For random and Center crop method you can just change the --crop_method 'random' to either stride or center.
If you use this repository please cite the following publication:
@article{10.1117/1.JMI.10.2.024004,
author = {Alexandros Patsanis and Mohammed R. S. Sunoqrot and Tone F. Bathen and Mattijs Elschot},
title = {{CROPro: a tool for automated cropping of prostate magnetic resonance images}},
volume = {10},
journal = {Journal of Medical Imaging},
number = {2},
publisher = {SPIE},
pages = {024004},
keywords = {deep learning, image cropping, image processing, prostate cancer, magnetic resonance imaging, Prostate, Education and training, Magnetic resonance imaging, Image segmentation, Performance modeling, Deep learning, Magnetism, Image classification, Principal component analysis, Visual process modeling},
year = {2023},
doi = {10.1117/1.JMI.10.2.024004},
URL = {https://doi.org/10.1117/1.JMI.10.2.024004}
}
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