Full Code of anjany/verse for AI

Repository: anjany/verse
Branch: main
Commit: 02b292b86021
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Directory structure:
gitextract_gbp5qvot/

├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── utils/
    ├── data_utilities.py
    ├── eval_utilities.py
    ├── evaluate.ipynb
    ├── prepare_data.ipynb
    └── sample/
        └── sub-verse004_seg-subreg_ctd.json

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
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================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2020 Anjany Kumar Sekuboyina

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

================================================
FILE: README.md
================================================
# VerSe: Large Scale Vertebrae Segmentation Challenge

_Look well to the spine for the cause of disease_ - Hippocrates

![VerSe examples. Observe the variability in data: field-of-view, fractures, transitional vertebrae, etc.](assets/dataset_snapshot.png)
*VerSe examples. Observe the variability in data: field-of-view, fractures, transitional vertebrae, etc.*

<details><summary>Table of Contents</summary><p>

* [What is VerSe?](#what-is-verse)
* [Citing VerSe](#citing-verse)
* [Data](#data)
* [Download](#download)
* [License](#license)
* [Code](#code)
* [Contact](#contact)
* [Other Related Work](#other-related-work)
* [Acknowledgements](#acknowledgements)

</p></details><p></p>

## What is VerSe?
Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource. However, a large-scale, public dataset is currently unavailable.

We believe *VerSe* can help here. VerSe is a large scale, multi-detector, multi-site, CT spine dataset consisting of 374 scans from 355 patients. The challenge was held in two iterations in conjunction with MICCAI 2019 and 2020. The tasks evaluated for include: vertebral labelling and segmentation.  

## Citing VerSe

If you use VerSe, we would appreciate references to the following papers. 

1. **Sekuboyina A et al., VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images, 2021.**<br />In Medical Image Analysis: https://doi.org/10.1016/j.media.2021.102166<br />Pre-print: https://arxiv.org/abs/2001.09193

2. **Löffler M et al., A Vertebral Segmentation Dataset with Fracture Grading. Radiology: Artificial Intelligence, 2020.**<br />In Radiology AI: https://doi.org/10.1148/ryai.2020190138

3. **Liebl H and Schinz D et al., A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data, 2021.**<br />Pre-print: https://arxiv.org/pdf/2103.06360.pdf


## Data

* The dataset has four files corresponding to one data sample: image, segmentation mask, centroid annotations, a PNG overview of the annotations.

* Data structure 
    - 01_training - Train data
    - 02_validation - (Formerly) PUBLIC test data
    - 03_test - (Formerly) HIDDEN test data

* Sub-directory-based arrangement for each patient. File names are constructed of entities, a suffix and a file extension following the conventions of the Brain Imaging Data Structure (BIDS; https://bids.neuroimaging.io/)

```
Example:
-------
training/rawdata/sub-verse000
    sub-verse000_dir-orient_ct.nii.gz - CT image series

training/derivatives/sub-verse000/
    sub-verse000_dir-orient_seg-vert_msk.nii.gz - Segmentation mask of the vertebrae
    sub-verse000_dir-orient_seg-subreg_ctd.json - Centroid coordinates in image space
    sub-verse000_dir-orient_seg-vert_snp.png - Preview reformations of the annotated CT data.

```


* Centroid coordinates of the subject based structure (.json file) are given in voxels in the image space. 'label' corresponds to the vertebral label: 
    - 1-7: cervical spine: C1-C7 
    - 8-19: thoracic spine: T1-T12 
    - 20-25: lumbar spine: L1-L6 
    - 26: sacrum - not labeled in this dataset 
    - 27: cocygis - not labeled in this dataset 
    - 28: additional 13th thoracic vertebra, T13


## Download

### WGET:

1. (VerSe'19) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse19training.zip
2. (VerSe'19) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse19validation.zip
3. (VerSe'19) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse19test.zip

4. (VerSe'20) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse20training.zip
5. (VerSe'20) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse20validation.zip
6. (VerSe'20) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse20test.zip

### OSF Reporsitories:

1. VerSe'19: https://osf.io/nqjyw/
2. VerSe'20: https://osf.io/t98fz/

**Note**: The annotation format of the complete VerSe data is **NOT** identical to the one used for the MICCAI challenges. The OSF repositories above also point to the MICCAI version of the data and annotations. Nonetheless, **we recommend usage of the restructured data and annotations**

## License
The data is provided under the [CC BY-SA 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/), making it fully open-sourced.

The rest of this repository is under the [MIT License](https://choosealicense.com/licenses/mit/).

## Code

We provide helper code and guiding notebooks.

* Data reading, standardising, and writing: [Data utilities](https://github.com/anjany/verse/blob/main/utils/data_utilities.py)
* Evaluation (as employed in the 2020 challenge): [Evaluation utilities](https://github.com/anjany/verse/blob/main/utils/eval_utilities.py)  
* Notebooks: [Data preperation](https://github.com/anjany/verse/blob/main/utils/prepare_data.ipynb), [Evaluation](https://github.com/anjany/verse/blob/main/utils/evaluate.ipynb)

## Contact
For queries and issues not fit for a github issue, please email [Anjany Sekuboyina](mailto:anjany.sekuboyina@tum.de) or [Jan Kirschke](mailto:jan.kirschke@tum.de) .


## Other Related Work

VerSe has resulted in numerous other publications. Below are a few selected ones.

**Sekuboyina A. et al., Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy. Radiology: Artificial Intelligence, 2020.** (https://doi.org/10.1148/ryai.2020190074)


## Acknowledgements
* This work is supported by the European Research Council (ERC) under the European Union’s ‘Horizon2020’  research  &  innovation  programme  (GA637164–iBack–ERC–2014–STG).
* We thank NVIDIA for the support in challenge organisation with compute. 
* README derived from [PCam](https://github.com/basveeling/pcam).


================================================
FILE: requirements.txt
================================================
matplotlib==3.3.4
Pillow>=8.2.0
scipy==1.6.1
numpy==1.20.1
nibabel==3.2.1


================================================
FILE: utils/data_utilities.py
================================================
"""data_utilities.py: Everything data-related for VerSe."""

__author__      = "Maximilian T. Löffler, Malek El Husseini"


from pathlib import Path
from numpy.core.numeric import NaN
import numpy as np
import nibabel as nib
import nibabel.processing as nip
import nibabel.orientations as nio
from scipy.ndimage import center_of_mass
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, Normalize
from matplotlib.patches import Circle
import json


v_dict = {
    1: 'C1', 2: 'C2', 3: 'C3', 4: 'C4', 5: 'C5', 6: 'C6', 7: 'C7',
    8: 'T1', 9: 'T2', 10: 'T3', 11: 'T4', 12: 'T5', 13: 'T6', 14: 'T7',
    15: 'T8', 16: 'T9', 17: 'T10', 18: 'T11', 19: 'T12', 20: 'L1',
    21: 'L2', 22: 'L3', 23: 'L4', 24: 'L5', 25: 'L6', 26: 'Sacrum',
    27: 'Cocc', 28: 'T13'
}

colors_itk = (1/255)*np.array([
    [255,  0,  0], [  0,255,  0], [  0,  0,255], [255,255,  0], [  0,255,255],
    [255,  0,255], [255,239,213],  # Label 1-7 (C1-7)
    [  0,  0,205], [205,133, 63], [210,180,140], [102,205,170], [  0,  0,128],
    [  0,139,139], [ 46,139, 87], [255,228,225], [106, 90,205], [221,160,221],
    [233,150,122], [165, 42, 42],  # Label 8-19 (T1-12)
    [255,250,250], [147,112,219], [218,112,214], [ 75,  0,130], [255,182,193],
    [ 60,179,113], [255,235,205],  # Label 20-26 (L1-6, sacrum)
    [255,235,205], [255,228,196],  # Label 27 cocc, 28 T13,
    [218,165, 32], [  0,128,128], [188,143,143], [255,105,180],  
    [255,  0,  0], [  0,255,  0], [  0,  0,255], [255,255,  0], [  0,255,255],
    [255,  0,255], [255,239,213],  # 29-39 unused
    [  0,  0,205], [205,133, 63], [210,180,140], [102,205,170], [  0,  0,128],
    [  0,139,139], [ 46,139, 87], [255,228,225], [106, 90,205], [221,160,221],
    [233,150,122],   # Label 40-50 (subregions)
    [255,250,250], [147,112,219], [218,112,214], [ 75,  0,130], [255,182,193],
    [ 60,179,113], [255,235,205], [255,105,180], [165, 42, 42], [188,143,143],
    [255,235,205], [255,228,196], [218,165, 32], [  0,128,128] # rest unused     
    ])
cm_itk = ListedColormap(colors_itk)
cm_itk.set_bad(color='w', alpha=0)  # set NaN to full opacity for overlay

# define HU windows
wdw_sbone = Normalize(vmin=-500, vmax=1300, clip=True)
wdw_hbone = Normalize(vmin=-200, vmax=1000, clip=True)

#########################
# Resample and reorient #


def reorient_to(img, axcodes_to=('P', 'I', 'R'), verb=False):
    """Reorients the nifti from its original orientation to another specified orientation
    
    Parameters:
    ----------
    img: nibabel image
    axcodes_to: a tuple of 3 characters specifying the desired orientation
    
    Returns:
    ----------
    newimg: The reoriented nibabel image 
    
    """
    aff = img.affine
    arr = np.asanyarray(img.dataobj, dtype=img.dataobj.dtype)
    ornt_fr = nio.io_orientation(aff)
    ornt_to = nio.axcodes2ornt(axcodes_to)
    ornt_trans = nio.ornt_transform(ornt_fr, ornt_to)
    arr = nio.apply_orientation(arr, ornt_trans)
    aff_trans = nio.inv_ornt_aff(ornt_trans, arr.shape)
    newaff = np.matmul(aff, aff_trans)
    newimg = nib.Nifti1Image(arr, newaff)
    if verb:
        print("[*] Image reoriented from", nio.ornt2axcodes(ornt_fr), "to", axcodes_to)
    return newimg


def resample_nib(img, voxel_spacing=(1, 1, 1), order=3):
    """Resamples the nifti from its original spacing to another specified spacing
    
    Parameters:
    ----------
    img: nibabel image
    voxel_spacing: a tuple of 3 integers specifying the desired new spacing
    order: the order of interpolation
    
    Returns:
    ----------
    new_img: The resampled nibabel image 
    
    """
    # resample to new voxel spacing based on the current x-y-z-orientation
    aff = img.affine
    shp = img.shape
    zms = img.header.get_zooms()
    # Calculate new shape
    new_shp = tuple(np.rint([
        shp[0] * zms[0] / voxel_spacing[0],
        shp[1] * zms[1] / voxel_spacing[1],
        shp[2] * zms[2] / voxel_spacing[2]
        ]).astype(int))
    new_aff = nib.affines.rescale_affine(aff, shp, voxel_spacing, new_shp)
    new_img = nip.resample_from_to(img, (new_shp, new_aff), order=order, cval=-1024)
    print("[*] Image resampled to voxel size:", voxel_spacing)
    return new_img


def resample_mask_to(msk, to_img):
    """Resamples the nifti mask from its original spacing to a new spacing specified by its corresponding image
    
    Parameters:
    ----------
    msk: The nibabel nifti mask to be resampled
    to_img: The nibabel image that acts as a template for resampling
    
    Returns:
    ----------
    new_msk: The resampled nibabel mask 
    
    """
    to_img.header['bitpix'] = 8
    to_img.header['datatype'] = 2  # uint8
    new_msk = nib.processing.resample_from_to(msk, to_img, order=0)
    print("[*] Mask resampled to image size:", new_msk.header.get_data_shape())
    return new_msk


def get_plane(img_path):
    """Gets the plane of the highest resolution from a nifti file
    
    Parameters:
    ----------
    img_path: the full path to the nifti file
    
    Returns:
    ----------
    plane: a string corresponding to the plane of highest resolution
    
    """
    plane_dict = {
        'S': 'ax', 'I': 'ax', 'L': 'sag', 'R': 'sag', 'A': 'cor', 'P': 'cor'}
    img = nib.load(str(img_path))
    axc = np.array(nio.aff2axcodes(img.affine))
    zms = np.around(img.header.get_zooms(), 1)
    ix_max = np.array(zms == np.amax(zms))
    num_max = np.count_nonzero(ix_max)
    if num_max == 2:
        plane = plane_dict[axc[~ix_max][0]]
    elif num_max == 1:
        plane = plane_dict[axc[ix_max][0]]
    else:
        plane = 'iso'
    return plane


######################
# Handling centroids #

def load_centroids(ctd_path):
    """loads the json centroid file
    
    Parameters:
    ----------
    ctd_path: the full path to the json file
    
    Returns:
    ----------
    ctd_list: a list containing the orientation and coordinates of the centroids
    
    """
    with open(ctd_path) as json_data:
        dict_list = json.load(json_data)
        json_data.close()
    ctd_list = []
    for d in dict_list:
        if 'direction' in d:
            ctd_list.append(tuple(d['direction']))
        elif 'nan' in str(d):            #skipping NaN centroids
            continue
        else:
            ctd_list.append([d['label'], d['X'], d['Y'], d['Z']]) 
    return ctd_list


def centroids_to_dict(ctd_list):
    """Converts the centroid list to a dictionary of centroids
    
    Parameters:
    ----------
    ctd_list: the centroid list
    
    Returns:
    ----------
    dict_list: a dictionart of centroids having the format dict[vertebra] = ['X':x, 'Y':y, 'Z': z]
    
    """
    dict_list = []
    for v in ctd_list:
        if any('nan' in str(v_item) for v_item in v): continue   #skipping invalid NaN values
        v_dict = {}
        if isinstance(v, tuple):
            v_dict['direction'] = v
        else:
            v_dict['label'] = int(v[0])
            v_dict['X'] = v[1]
            v_dict['Y'] = v[2]
            v_dict['Z'] = v[3]
        dict_list.append(v_dict)
    return dict_list


def save_centroids(ctd_list, out_path):
    """Saves the centroid list to json file
    
    Parameters:
    ----------
    ctd_list: the centroid list
    out_path: the full desired save path
    
    """
    if len(ctd_list) < 2:
        print("[#] Centroids empty, not saved:", out_path)
        return
    json_object = centroids_to_dict(ctd_list)
    # Problem with python 3 and int64 serialisation.
    def convert(o):
        if isinstance(o, np.int64):
            return int(o)
        raise TypeError
    with open(out_path, 'w') as f:
        json.dump(json_object, f, default=convert)
    print("[*] Centroids saved:", out_path)


def calc_centroids(msk, decimals=1, world=False):
    """Gets the centroids from a nifti mask by calculating the centers of mass of each vertebra
    
    Parameters:
    ----------
    msk: nibabel nifti mask
    decimals: rounds the coordinates x decimal digits
    
    Returns:
    ----------
    ctd_list: list of centroids 
    
    """
    msk_data = np.asanyarray(msk.dataobj, dtype=msk.dataobj.dtype)
    axc = nio.aff2axcodes(msk.affine)
    ctd_list = [axc]
    verts = np.unique(msk_data)[1:]
    verts = verts[~np.isnan(verts)]  # remove NaN values
    for i in verts:
        msk_temp = np.zeros(msk_data.shape, dtype=bool)
        msk_temp[msk_data == i] = True
        ctr_mass = center_of_mass(msk_temp)
        if world:
            ctr_mass = msk.affine[:3, :3].dot(ctr_mass) + msk.affine[:3, 3]
            ctr_mass = ctr_mass.tolist()
        ctd_list.append([i] + [round(x, decimals) for x in ctr_mass])
    return ctd_list


def reorient_centroids_to(ctd_list, img, decimals=1, verb=False):
    """reorient centroids to image orientation
    
    Parameters:
    ----------
    ctd_list: list of centroids
    img: nibabel image 
    decimals: rounding decimal digits
    
    Returns:
    ----------
    out_list: reoriented list of centroids 
    
    """
    ctd_arr = np.transpose(np.asarray(ctd_list[1:]))
    if len(ctd_arr) == 0:
        print("[#] No centroids present") 
        return ctd_list
    v_list = ctd_arr[0].astype(int).tolist()  # vertebral labels
    ctd_arr = ctd_arr[1:]
    ornt_fr = nio.axcodes2ornt(ctd_list[0])  # original centroid orientation
    axcodes_to = nio.aff2axcodes(img.affine)
    ornt_to = nio.axcodes2ornt(axcodes_to)
    trans = nio.ornt_transform(ornt_fr, ornt_to).astype(int)
    perm = trans[:, 0].tolist()
    shp = np.asarray(img.dataobj.shape)
    ctd_arr[perm] = ctd_arr.copy()
    for ax in trans:
        if ax[1] == -1:
            size = shp[ax[0]]
            ctd_arr[ax[0]] = np.around(size - ctd_arr[ax[0]], decimals)
    out_list = [axcodes_to]
    ctd_list = np.transpose(ctd_arr).tolist()
    for v, ctd in zip(v_list, ctd_list):
        out_list.append([v] + ctd)
    if verb:
        print("[*] Centroids reoriented from", nio.ornt2axcodes(ornt_fr), "to", axcodes_to)
    return out_list


def rescale_centroids(ctd_list, img, voxel_spacing=(1, 1, 1)):
    """rescale centroid coordinates to new spacing in current x-y-z-orientation
    
    Parameters:
    ----------
    ctd_list: list of centroids
    img: nibabel image 
    voxel_spacing: desired spacing
    
    Returns:
    ----------
    out_list: rescaled list of centroids 
    
    """
    ornt_img = nio.io_orientation(img.affine)
    ornt_ctd = nio.axcodes2ornt(ctd_list[0])
    if np.array_equal(ornt_img, ornt_ctd):
        zms = img.header.get_zooms()
    else:
        ornt_trans = nio.ornt_transform(ornt_img, ornt_ctd)
        aff_trans = nio.inv_ornt_aff(ornt_trans, img.dataobj.shape)
        new_aff = np.matmul(img.affine, aff_trans)
        zms = nib.affines.voxel_sizes(new_aff)
    ctd_arr = np.transpose(np.asarray(ctd_list[1:]))
    v_list = ctd_arr[0].astype(int).tolist()  # vertebral labels
    ctd_arr = ctd_arr[1:]
    ctd_arr[0] = np.around(ctd_arr[0] * zms[0] / voxel_spacing[0], decimals=1)
    ctd_arr[1] = np.around(ctd_arr[1] * zms[1] / voxel_spacing[1], decimals=1)
    ctd_arr[2] = np.around(ctd_arr[2] * zms[2] / voxel_spacing[2], decimals=1)
    out_list = [ctd_list[0]]
    ctd_list = np.transpose(ctd_arr).tolist()
    for v, ctd in zip(v_list, ctd_list):
        out_list.append([v] + ctd)
    print("[*] Rescaled centroid coordinates to spacing (x, y, z) =", voxel_spacing, "mm")
    return out_list

def create_figure(dpi, *planes):
    """creates a matplotlib figure
    
    Parameters:
    ----------
    dpi: desired dpi
    *planes: numpy arrays to include in the figure 
    
    Returns:
    ----------
    fig, axs
    
    """
    fig_h = round(2 * planes[0].shape[0] / dpi, 2)
    plane_w = [p.shape[1] for p in planes]
    w = sum(plane_w)
    fig_w = round(2 * w / dpi, 2)
    x_pos = [0]
    for x in plane_w[:-1]:
        x_pos.append(x_pos[-1] + x)
    fig, axs = plt.subplots(1, len(planes), figsize=(fig_w, fig_h))
    for a in axs:
        a.axis('off')
        idx = axs.tolist().index(a)
        a.set_position([x_pos[idx]/w, 0, plane_w[idx]/w, 1])
    return fig, axs


def plot_sag_centroids(axs, ctd, zms):
    """plots sagittal centroids on a plane axes
    
    Parameters:
    ----------
    axs: matplotlib axs
    ctd: list of centroids
    zms: the spacing of the image
    """
    # requires v_dict = dictionary of mask labels
    for v in ctd[1:]:
        axs.add_patch(Circle((v[2]*zms[1], v[1]*zms[0]), 2, color=colors_itk[v[0]-1]))
        axs.text(4, v[1]*zms[0], v_dict[v[0]], fontdict={'color': cm_itk(v[0]-1), 'weight': 'bold'})


def plot_cor_centroids(axs, ctd, zms):
    """plots coronal centroids on a plane axes
    
    Parameters:
    ----------
    axs: matplotlib axs
    ctd: list of centroids
    zms: the spacing of the image
    """
    # requires v_dict = dictionary of mask labels
    for v in ctd[1:]:
        axs.add_patch(Circle((v[3]*zms[2], v[1]*zms[0]), 2, color=colors_itk[v[0]-1]))
        axs.text(4, v[1]*zms[0], v_dict[v[0]], fontdict={'color': cm_itk(v[0]-1), 'weight': 'bold'})

================================================
FILE: utils/eval_utilities.py
================================================
"""eval_utilities.py: Everything evaluation-related for VerSe."""

__author__      = "Anjany Sekuboyina"



import numpy as np

# -------
# dice
# -------

def compute_dice(im1, im2, empty_score=1.0):
    """
    Computes the Dice coefficient, a measure of set similarity.
    Parameters
    ----------
    im1 : array-like, bool
        Any array of arbitrary size. If not boolean, will be converted.
    im2 : array-like, bool
        Any other array of identical size. If not boolean, will be converted.
    Returns
    -------
    dice : float
        Dice coefficient as a float on range [0,1].
        Maximum similarity = 1
        No similarity = 0
        Both are empty (sum eq to zero) = empty_score

    Notes
    -----
    The order of inputs for `dice` is irrelevant. The result will be
    identical if `im1` and `im2` are switched.
    """
    im1 = np.asarray(im1).astype(np.bool)
    im2 = np.asarray(im2).astype(np.bool)

    if im1.shape != im2.shape:
        raise ValueError("Shape mismatch: im1 and im2 must have the same shape.")

    im_sum = im1.sum() + im2.sum()
    if im_sum == 0:
        return empty_score

    # Compute Dice coefficient
    intersection = np.logical_and(im1, im2)

    return 2. * intersection.sum() / im_sum



# -------
# id.rate
# -------

def construct_distance_matrix(act, pred, max_vert_idx):
    # computing the distance matrix. Should be a N*N matrix -- distance
    # from every vertebrae to every other vetabrae
    act_stack = np.transpose(np.repeat(np.expand_dims(act, -1), max_vert_idx, axis=2), [2, 1, 0])
    pred_stack = np.repeat(np.expand_dims(pred, -1), max_vert_idx, axis=2)
    d_mat = np.sqrt(np.sum(np.square(pred_stack - act_stack), axis=1))
    return d_mat


def get_hits(cent_list_gt, cent_list_pred, max_vert_idx):

    """
    Computes vertebrae identification stats
    Parameters
    ----------
    cent_list_gt : array-like, float
        Any array of shape (max_vert_idx, 3), each row containing the 3D coordinates of the vertebrae
        NaN if a vertebrae is absent. 
    cent_list_pred : array-like, float
        Predicted centroids. Same as cent_list_gt
    max_vert_idx : int
        Maximum vertebral index that the code is dealing with.

    Returns
    -------
    hits : int
        Count of the number of successful identifcations
        Successful identification defined as the correct label
        being closest and < 20mm away.
    hit_list: array-like
        A 1-d array of lenght max_vert_idx. Contains nan (if vertebra is absent in GT),
        1 (if vertebra is succesfully identified), and 0 (if failed to identify)

    Notes
    -----
    Return hits = 0 if no successful identification.
    """

    hit_list = np.full(max_vert_idx, np.nan)

    # vertebrae present according to json labels
    verts_in_im = np.argwhere(~np.isnan(cent_list_gt[:, 0])) + 1
    verts_in_pred = np.argwhere(~np.isnan(cent_list_pred[:, 0])) + 1

    hit_list[verts_in_im - 1] = 0

    # intersection of vertebrae in actual and predicted jsons
    intersect_verts = np.intersect1d(verts_in_im, verts_in_pred)

    if intersect_verts.size != 0:
        # construct distance_matrix
        d_mat = construct_distance_matrix(cent_list_gt, cent_list_pred, max_vert_idx)
        d_mat_verts = d_mat[intersect_verts - 1, :][:, intersect_verts - 1]

        # ID rates
        # 1. check if pred landmark is closest to actual landmark
        mask = np.ones_like(d_mat_verts, dtype=bool)
        mask[range(mask.shape[0]), np.argmin(d_mat_verts, axis=1)] = False
        d_mat_verts[mask] = np.nan

        # 2. after check above, check if the predicted landmark is less
        # than 20 mm away.
        d_id_verts = np.copy(np.diagonal(d_mat_verts))
        d_id_verts[d_id_verts > 20.] = np.nan

        hits = np.count_nonzero(~np.isnan(d_id_verts))
        hit_list[intersect_verts[~np.isnan(d_id_verts)] - 1] = 1
        return hits, hit_list
    else:
        return 0., hit_list


================================================
FILE: utils/evaluate.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "periodic-optics",
   "metadata": {},
   "source": [
    "## Evaluation Utilities\n",
    "\n",
    "This notebook guides provides a sample evaluation walktrough for identification rate (id.rate for labelling)\n",
    "and Dice score (for segmentation). \n",
    "\n",
    "Please look at the data_utilities notebook for data-preperation!  \n",
    "\n",
    "**Note**: We do not provide scripts for Hausdorff distance and localisation distance.\n",
    "    Eventhough they were used for the benchamrking process, these were not used for VerSe'20. \n",
    "    This is because of their invalidity when evaluating 'missed vertebrae'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dried-electron",
   "metadata": {},
   "outputs": [],
   "source": [
    "## imports\n",
    "\n",
    "# libraries\n",
    "import os\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import nibabel.orientations as nio\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# custom\n",
    "import data_utilities as dutils\n",
    "import eval_utilities as eutils "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "specialized-ladder",
   "metadata": {},
   "outputs": [],
   "source": [
    "## paths\n",
    "\n",
    "directory = os.path.join(os.getcwd(),'sample')\n",
    "\n",
    "true_msk = nib.load(os.path.join(directory,'sub-verse004_seg-vert_msk.nii.gz')) \n",
    "pred_msk = nib.load(os.path.join(directory,'sub-verse004_seg-vert_msk.nii.gz')) # use the same file for example\n",
    "\n",
    "true_ctd = dutils.load_centroids(os.path.join(directory,'sub-verse004_seg-subreg_ctd.json'))\n",
    "pred_ctd = dutils.load_centroids(os.path.join(directory,'sub-verse004_seg-subreg_ctd.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "parliamentary-kazakhstan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] Image resampled to voxel size: (1, 1, 1)\n",
      "[*] Rescaled centroid coordinates to spacing (x, y, z) = (1, 1, 1) mm\n",
      "[*] Image resampled to voxel size: (1, 1, 1)\n",
      "[*] Rescaled centroid coordinates to spacing (x, y, z) = (1, 1, 1) mm\n"
     ]
    }
   ],
   "source": [
    "## pre-process (evaluation was done at 1mm because annotations were performed at 1mm)\n",
    "\n",
    "true_msk = dutils.resample_nib(true_msk, voxel_spacing=(1, 1, 1), order=0) # or resample based on img: resample_mask_to(msk_nib, img_iso)\n",
    "true_ctd = dutils.rescale_centroids(true_ctd, true_msk, (1,1,1))\n",
    "\n",
    "pred_msk = dutils.resample_nib(pred_msk, voxel_spacing=(1, 1, 1), order=0) # or resample based on img: resample_mask_to(msk_nib, img_iso)\n",
    "pred_ctd = dutils.rescale_centroids(pred_ctd, pred_msk, (1,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "distinct-cornell",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dice:1.00\n"
     ]
    }
   ],
   "source": [
    "## compute dice\n",
    "\n",
    "true_msk_arr =  true_msk.get_fdata()\n",
    "pred_msk_arr =  pred_msk.get_fdata()\n",
    "\n",
    "\n",
    "dice = eutils.compute_dice(pred_msk_arr, true_msk_arr)\n",
    "\n",
    "print('Dice:{:.2f}'.format(dice))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "private-affiliate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id.rate:1.00\n",
      "\n",
      "Hits:\n",
      " [nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1. nan nan nan nan]\n"
     ]
    }
   ],
   "source": [
    "## compute id_rate\n",
    "\n",
    "MAX_VERT_IDX = 28 # (in VerSe20, T13 has an index of 28)\n",
    "\n",
    "# create an array of shape (MAX_VERT_IDX, 3), \n",
    "# i_th row contain centroid for (i+1)_th vertebra. Rest are NaNs\n",
    "\n",
    "def prepare_ctd_array(ctd_list, max_vert_idx):\n",
    "    ctd_arr = np.full((max_vert_idx, 3), np.nan)\n",
    "    for item in ctd_list[1:]: # first entry contains orientation \n",
    "        vert_idx = item[0]\n",
    "        if vert_idx <= max_vert_idx:\n",
    "            X = item[1]\n",
    "            Y = item[2]\n",
    "            Z = item[3]\n",
    "            ctd_arr[vert_idx - 1, :] = [X, Y, Z]\n",
    "    return ctd_arr\n",
    "\n",
    "true_ctd_arr =  prepare_ctd_array(true_ctd, MAX_VERT_IDX)\n",
    "pred_ctd_arr =  prepare_ctd_array(pred_ctd, MAX_VERT_IDX)\n",
    "\n",
    "# get number of successful hits (identifications)\n",
    "\n",
    "num_hits, hit_list = eutils.get_hits(true_ctd_arr, pred_ctd_arr, MAX_VERT_IDX)\n",
    "verts_in_gt        = np.argwhere(~np.isnan(true_ctd_arr[:, 0])).reshape(-1) + 1  # list of vertebrae present in annotation\n",
    "\n",
    "print('id.rate:{:.2f}\\n'.format(num_hits/len(verts_in_gt)))\n",
    "print('Hits:\\n', hit_list) # nan : vertebrae is absent. 1 : successful identifcation. 0 : failed identification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "million-queen",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: utils/prepare_data.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Utilities\n",
    "\n",
    "This notebook guides one through the following tasks:\n",
    "\n",
    "1. Reading the image, segmentation mask, and centroid annotation.\n",
    "2. Processing them to a standard orientation and spacing.\n",
    "3. Visualising them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## imports\n",
    "\n",
    "# libraries\n",
    "import os\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import nibabel.orientations as nio\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# custom\n",
    "from data_utilities import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load a datasample: image, mask, and centroid JSON "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## paths\n",
    "\n",
    "# data directory\n",
    "directory = os.path.join(os.getcwd(),'sample')\n",
    "\n",
    "# load files\n",
    "img_nib = nib.load(os.path.join(directory,'sub-verse004_ct.nii.gz'))\n",
    "msk_nib = nib.load(os.path.join(directory,'sub-verse004_seg-vert_msk.nii.gz'))\n",
    "ctd_list = load_centroids(os.path.join(directory,'sub-verse004_seg-subreg_ctd.json'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Check the image orientation, spacing, and the centroid coordinates out. \n",
    "\n",
    "Note: The centroids are in the same orientation and spacing as the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "img zooms = (1.0, 1.0, 2.000296)\n",
      "img orientation code: ('P', 'I', 'R')\n",
      "Centroid List: [('P', 'I', 'R'), [16, 79.1, 40.9, 32.3], [17, 79.3, 63.4, 32.2], [18, 76.0, 88.6, 32.1], [19, 70.6, 117.3, 31.6], [20, 63.2, 141.9, 31.8], [21, 53.0, 167.7, 30.8], [22, 46.5, 202.0, 29.8], [23, 46.8, 235.3, 29.1], [24, 57.2, 266.4, 29.3]]\n"
     ]
    }
   ],
   "source": [
    "#check img zooms \n",
    "zooms = img_nib.header.get_zooms()\n",
    "print('img zooms = {}'.format(zooms))\n",
    "\n",
    "#check img orientation\n",
    "axs_code = nio.ornt2axcodes(nio.io_orientation(img_nib.affine))\n",
    "print('img orientation code: {}'.format(axs_code))\n",
    "\n",
    "#check centroids\n",
    "print('Centroid List: {}'.format(ctd_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Rescale and reorient\n",
    "\n",
    "- Rescale and reorient image and mask to 1mm spacing and then reorient them to IPL. \n",
    "- Based on the new image, reorient and resample centroids too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] Image resampled to voxel size: (1, 1, 1)\n",
      "[*] Image resampled to voxel size: (1, 1, 1)\n",
      "[*] Rescaled centroid coordinates to spacing (x, y, z) = (1, 1, 1) mm\n",
      "img zooms = (1.0, 1.0, 1.0)\n",
      "img orientation code: ('I', 'P', 'L')\n",
      "new centroids: [('I', 'P', 'L'), [16, 40.9, 79.1, 57.4], [17, 63.4, 79.3, 57.6], [18, 88.6, 76.0, 57.8], [19, 117.3, 70.6, 58.8], [20, 141.9, 63.2, 58.4], [21, 167.7, 53.0, 60.4], [22, 202.0, 46.5, 62.4], [23, 235.3, 46.8, 63.8], [24, 266.4, 57.2, 63.4]]\n"
     ]
    }
   ],
   "source": [
    "# Resample and Reorient data\n",
    "img_iso = resample_nib(img_nib, voxel_spacing=(1, 1, 1), order=3)\n",
    "msk_iso = resample_nib(msk_nib, voxel_spacing=(1, 1, 1), order=0) # or resample based on img: resample_mask_to(msk_nib, img_iso)\n",
    "ctd_iso = rescale_centroids(ctd_list, img_nib, (1,1,1))\n",
    "\n",
    "img_iso = reorient_to(img_iso, axcodes_to=('I', 'P', 'L'))\n",
    "msk_iso = reorient_to(msk_iso, axcodes_to=('I', 'P', 'L'))\n",
    "ctd_iso = reorient_centroids_to(ctd_iso, img_iso)\n",
    "\n",
    "#check img zooms \n",
    "zooms = img_iso.header.get_zooms()\n",
    "print('img zooms = {}'.format(zooms))\n",
    "\n",
    "#check img orientation\n",
    "axs_code = nio.ornt2axcodes(nio.io_orientation(img_iso.affine))\n",
    "print('img orientation code: {}'.format(axs_code))\n",
    "\n",
    "#check centroids\n",
    "print('new centroids: {}'.format(ctd_iso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualise the processed image and annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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