dicom_to_cnn.model.petctviewer package

Submodules

dicom_to_cnn.model.petctviewer.CsvReader module

class dicom_to_cnn.model.petctviewer.CsvReader.CsvReader(path: str)

Bases: object

CSV Parser for CSV generated by petctviewer.org

classmethod convert_manual_row_to_object(manual_row: list, results_row: list) → dict

Return a row manual row in an object with ROI details

Arguments:

manual_row ([list]) : manual row, where to find coordonates results_row ([list]) : results row, where to find name, roi_number, suv_mean, sd, suv_max, etc

Returns:

[dict] – dict describing the ROI

classmethod convert_nifti_row_to_object(nifti_row: list, results_row: list) → dict

Return list of point included in the roi

Arguments:

nifti_row ([list]) : nifti row of the csv, where to find coordonates results_row ([list]) : results row, where to find name, roi_number, suv_mean, sd, suv_max, etc

Returns:

[dict] – dict describing the ROI

get_SUL() → str

return the SUL value of the CSV

get_SUVlo() → str

return the SUVlo value of the CSV

get_csv_roi_details() → dict

return a dict with each ROI information to build the mask

Returns:

([dict]){(roi)1{dict generated by convert_manual_row_to_object() or convert_nifti_row_to_object() },

(roi)2 : // , … ‘SUL’ : value , ‘SUVlo’ : value}

get_date() → str

return date in csv file

Returns:

[str]: [return date in csv file]

get_manual_rois() → list

return manual rois block

get_nifti_rois() → list

return automatic (nifti) roi block

get_patient_id() → str

return patient id in csv file

Returns:

[str]: [retuen patient id in csv file]

get_rois_results() → list

Return CSV lines containing ROIs results

get_sum_row() → list

return csv line containing sum rois volume

get_time() → str

return time in csv file

Returns:

[str]: [return time in csv file]

classmethod list_string_to_point_list_int(point_list_string: list) → list

Transforms point string into list of coordinate (int)

Arguments:

point_list_strings ([list]) : list of point string

Returns:

point_list [(list)] : [return list of coordonates (int)]

dicom_to_cnn.model.petctviewer.MaskBuilder module

dicom_to_cnn.model.petctviewer.Roi module

class dicom_to_cnn.model.petctviewer.Roi.Roi(axis: int, first_slice: int, last_slice: int, roi_number: int, type_number: int, list_point: list, volume_dimension: tuple)

Bases: object

A class to represent a ROI

coronal_to_axial(np_array_3D: numpy.ndarray) → numpy.ndarray

transform coronal 3d ndarray to 3d axial ndarray

Args:

np_array_3D (np.ndarray): [ROI ndarray]]

Returns:

[np.ndarray]: [return axial ndarray]

get_empty_np_array() → numpy.ndarray

Return numpy array to fill given the current dimension and axis

Returns:

[np.ndarray] – zero filled numpy array

get_mask(list_points: list) → numpy.ndarray

generate an empty ndarray and fill up with ROI coordonates

Args:

list_points (list): [ [[x,y,z], [x,y,z], [x,y,z], …] ]

Returns:

[np.ndarray]: [return binary ndarray of the ROI]

mask_roi_in_slice(patch: <module 'matplotlib.patches' from '/home/docs/.pyenv/versions/3.7.9/lib/python3.7/site-packages/matplotlib/patches.py'>) → list

get ROI x and y limits in which we will loop, to gather [x,y] pixel which are in the patch

Args:

patch (matplotlib.patches): [polygon or ellipse]]

Returns:

[list]: [list of [x,y] coordonates]

sagittal_to_axial(np_array_3D: numpy.ndarray) → numpy.ndarray

transform saggital 3d ndarray to 3d axial ndarray

Args:

np_array_3D (np.ndarray): [ROI ndarray]]

Returns:

[np.ndarray]: [return axial ndarray]

dicom_to_cnn.model.petctviewer.RoiElipse module

class dicom_to_cnn.model.petctviewer.RoiElipse.RoiElipse(axis: int, first_slice: int, last_slice: int, roi_number: int, type_number: int, list_point: list, volume_dimension: tuple)

Bases: dicom_to_cnn.model.petctviewer.Roi.Roi

Derivated Class for manual Elipse ROI of PetCtViewer.org

Returns:

[RoiElipse] – Roi Elipse Object

calculateMaskPoint()

calculate [x,y,z] coordonates/voxel which belong to the ROI in ellipse patches

Returns:

[list]: [list of [x,y,z] coordonates of ROI ]

dicom_to_cnn.model.petctviewer.RoiFactory module

class dicom_to_cnn.model.petctviewer.RoiFactory.RoiFactory(details: dict, volume_dimension: tuple, roi_number: int)

Bases: object

Create ROI object from a CSV Roi details

Returns:

[RoiFactory] – Factory for ROI instanciation according to csv details

read_roi() → dicom_to_cnn.model.petctviewer.Roi.Roi

Instanciate the Correct ROI type according to data

Returns:

[Roi] – Roi object

dicom_to_cnn.model.petctviewer.RoiNifti module

class dicom_to_cnn.model.petctviewer.RoiNifti.RoiNifti(roi_number: int, list_point: list, volume_dimension: tuple)

Bases: dicom_to_cnn.model.petctviewer.Roi.Roi

Derivated Class for automatic Nifti ROI of PetCtViewer.org

Returns:

[RoiNifti] – Nifti ROI

calculateMaskPoint() → list

Return [x,y,z] coordonates/voxel of nifti ROI (already in CSV file)

Returns:

[list]: [list of [x,y,z] coordonates of ROI ]

dicom_to_cnn.model.petctviewer.RoiPolygon module

class dicom_to_cnn.model.petctviewer.RoiPolygon.RoiPolygon(axis: int, first_slice: int, last_slice: int, roi_number: int, type_number: int, list_point: list, volume_dimension: tuple)

Bases: dicom_to_cnn.model.petctviewer.Roi.Roi

Derivated Class for manual Polygon ROI of PetCtViewer.org

Returns:

[RoiPolygon] – Polygone ROI

calculateMaskPoint() → list

calculate [x,y,z] coordonates/voxel which belong to the ROI in polygon patches

Returns:

[list]: [list of [x,y,z] coordonates of ROI]

dicom_to_cnn.model.petctviewer.csv_volume module

dicom_to_cnn.model.petctviewer.csv_volume.get_difference_volume(csv_path: str) → tuple

Calcul the difference between volume and sum of every ROI volume

Args:

csv_path ([str]): [CSV file path]

Returns:

[tuple]: [Return total_volume, sum of every ROIs volume, and difference between the 2]

dicom_to_cnn.model.petctviewer.csv_volume.get_sum_rois_volume(list_results_row: list) → float

sum all ROIs volume

Args:

liste ([list]): [List of every ROI results row from CSV file]

Returns:

[float]: [Return the sum of ROIs volume]

dicom_to_cnn.model.petctviewer.csv_volume.get_total_volume(csv_path: str) → float

get total volume of all ROI in CSV file

Args:

csv_path (str): [path of the CSV file]

Returns:

[float]: [total volume of tumor]

dicom_to_cnn.model.petctviewer.csv_volume.keep_csv_higher_volume(liste_csv_path: list) → list

function to

Args:

liste_csv_path ([list]): [list of CSV file path]

Returns:

([list]) : [return the CSV file path with the higher volume]

Module contents