Minor changes and bug fixes

MEDimage/wrangling/DataManager.py

@@ -283,7 +283,7 @@ def __read_all_dicoms(self) -> None:
         # ASSOCIATE ALL VOLUMES TO THEIR MASK
         self.__associate_rt_stuct()
 
-    def process_all_dicoms(self) -> List[MEDimage]:
+    def process_all_dicoms(self) -> Union[List[MEDimage], None]:
         """This function reads the DICOM content of all the sub-folder tree of a starting directory defined by
         `path_to_dicoms`. It then organizes the data (files throughout the starting directory are associated by
         'SeriesInstanceUID') in the MEDimage class including the region of  interest (ROI) defined by an
@@ -408,7 +408,8 @@ def process_all_dicoms(self) -> List[MEDimage]:
                         self.instances = self.instances[:10]
         print('DONE')
 
-        return self.instances
+        if self.instances:
+            return self.instances
 
     def __read_all_niftis(self) -> None:
         """Reads all files in the initial path and organizes other path to images and roi
@@ -814,10 +815,11 @@ def __pre_radiomics_checks_dimensions(
             df_xy = pd.DataFrame(xy_dim["data"], columns=['data'])
             del xy_dim["data"]  # no interest in keeping data (we only need statistics)
             ax = df_xy.hist(column='data')
-            min_quant, max_quant= df_xy.quantile(min_percentile), df_xy.quantile(max_percentile)
+            min_quant, max_quant, average = df_xy.quantile(min_percentile), df_xy.quantile(max_percentile), df_xy.mean()
             for x in ax[0]:
                 x.axvline(min_quant.data, linestyle=':', color='r', label=f"Min Percentile: {float(min_quant):.3f}")
                 x.axvline(max_quant.data, linestyle=':', color='g', label=f"Max Percentile: {float(max_quant):.3f}")
+                x.axvline(average.data, linestyle='solid', color='gold', label=f"Average: {float(average.data):.3f}")
                 x.grid(False)
                 plt.title(f"Voxels xy-spacing checks for {wildcard}")
                 plt.legend()
@@ -827,10 +829,11 @@ def __pre_radiomics_checks_dimensions(
             df_z = pd.DataFrame(z_dim["data"], columns=['data'])
             del z_dim["data"]  # no interest in keeping data (we only need statistics)
             ax = df_z.hist(column='data')
-            min_quant, max_quant= df_z.quantile(min_percentile), df_z.quantile(max_percentile)
+            min_quant, max_quant, average = df_z.quantile(min_percentile), df_z.quantile(max_percentile), df_z.mean()
             for x in ax[0]:
                 x.axvline(min_quant.data, linestyle=':', color='r', label=f"Min Percentile: {float(min_quant):.3f}")
                 x.axvline(max_quant.data, linestyle=':', color='g', label=f"Max Percentile: {float(max_quant):.3f}")
+                x.axvline(average.data, linestyle='solid', color='gold', label=f"Average: {float(average.data):.3f}")
                 x.grid(False)
                 plt.title(f"Voxels z-spacing checks for {wildcard}")
                 plt.legend()
@@ -1344,21 +1347,23 @@ class zDim:
 
             # Plotting xy-spacing histogram
             ax = df_xy.hist(column='xyDim')
-            min_quant, max_quant= df_xy.quantile(min_percentile), df_xy.quantile(max_percentile)
+            min_quant, max_quant, average = df_xy.quantile(min_percentile), df_xy.quantile(max_percentile), param.xyDim.data.mean()
             for x in ax[0]:
                 x.axvline(min_quant.xyDim, linestyle=':', color='r', label=f"Min Percentile: {float(min_quant):.3f}")
                 x.axvline(max_quant.xyDim, linestyle=':', color='g', label=f"Max Percentile: {float(max_quant):.3f}")
+                x.axvline(average, linestyle='solid', color='gold', label=f"Average: {float(average):.3f}")
                 x.grid(False)
                 plt.title(f"MR xy-spacing imaging summary for {wildcard}")
                 plt.legend()
                 plt.show()
 
             # Plotting z-spacing histogram
             ax = df_z.hist(column='zDim')
-            min_quant, max_quant = df_z.quantile(min_percentile), df_z.quantile(max_percentile)
+            min_quant, max_quant, average = df_z.quantile(min_percentile), df_z.quantile(max_percentile), param.zDim.data.mean()
             for x in ax[0]:
                 x.axvline(min_quant.zDim, linestyle=':', color='r', label=f"Min Percentile: {float(min_quant):.3f}")
                 x.axvline(max_quant.zDim, linestyle=':', color='g', label=f"Max Percentile: {float(max_quant):.3f}")
+                x.axvline(average, linestyle='solid', color='gold', label=f"Average: {float(average):.3f}")
                 x.grid(False)
                 plt.title(f"MR z-spacing imaging summary for {wildcard}")
                 plt.legend()
@@ -1550,21 +1555,23 @@ class zDim:
 
             # Plotting xy-spacing histogram
             ax = df_xy.hist(column='xyDim')
-            min_quant, max_quant= df_xy.quantile(min_percentile), df_xy.quantile(max_percentile)
+            min_quant, max_quant, average = df_xy.quantile(min_percentile), df_xy.quantile(max_percentile), param.xyDim.data.mean()
             for x in ax[0]:
                 x.axvline(min_quant.xyDim, linestyle=':', color='r', label=f"Min Percentile: {float(min_quant):.3f}")
                 x.axvline(max_quant.xyDim, linestyle=':', color='g', label=f"Max Percentile: {float(max_quant):.3f}")
+                x.axvline(average, linestyle='solid', color='gold', label=f"Average: {float(average):.3f}")
                 x.grid(False)
                 plt.title(f"CT xy-spacing imaging summary for {wildcard}")
                 plt.legend()
                 plt.show()
 
             # Plotting z-spacing histogram
             ax = df_z.hist(column='zDim')
-            min_quant, max_quant = df_z.quantile(min_percentile), df_z.quantile(max_percentile)
+            min_quant, max_quant, average = df_z.quantile(min_percentile), df_z.quantile(max_percentile), param.zDim.data.mean()
             for x in ax[0]:
                 x.axvline(min_quant.zDim, linestyle=':', color='r', label=f"Min Percentile: {float(min_quant):.3f}")
                 x.axvline(max_quant.zDim, linestyle=':', color='g', label=f"Max Percentile: {float(max_quant):.3f}")
+                x.axvline(average, linestyle='solid', color='gold', label=f"Average: {float(average):.3f}")
                 x.grid(False)
                 plt.title(f"CT z-spacing imaging summary for {wildcard}")
                 plt.legend()

MEDimage/wrangling/ProcessDICOM.py

@@ -62,9 +62,9 @@ def __get_dicom_scan_orientation(self, dicom_header: List[pydicom.dataset.FileDa
         image_patient_positions_y = [dicom_header[i].ImagePositionPatient[1] for i in range(n_slices)]
         image_patient_positions_z = [dicom_header[i].ImagePositionPatient[2] for i in range(n_slices)]
         dist = [
-            max(np.abs(np.diff(image_patient_positions_x))), 
-            max(np.abs(np.diff(image_patient_positions_y))), 
-            max(np.abs(np.diff(image_patient_positions_z)))
+            np.median(np.abs(np.diff(image_patient_positions_x))), 
+            np.median(np.abs(np.diff(image_patient_positions_y))), 
+            np.median(np.abs(np.diff(image_patient_positions_z)))
         ]
         index = dist.index(max(dist))
         if index == 0:

README.md

@@ -46,6 +46,7 @@ For more installation options (conda, poetry...) check out the [installation doc
 We used sphinx to create the documentation for this project and you check it out in this [link](https://medimage.readthedocs.io/en/latest/). But you can generate and host it locally by compiling the documentation source code using:
 
 ```
+cd docs
 make clean
 make html
 ```

docs/tutorials.rst

@@ -6,10 +6,15 @@ Download dataset
     In all the tutorials, an open-access dataset will be used. It consists of medical images for different type of cancers (Glioma, sarcoma...)
     and with different imaging modalities (MR, CT and PET). This dataset has been pre-processed in order to be compliant with the package norms.
 
-    A script is made available to download the dataset and organize it in your local workspace, just run the following command in your terminal
+    A script is made available to download the dataset (~3.2 GB) and organize it in your local workspace, just run the following command in your terminal
     from the  package parent folder ::
 
        python scripts/download_data.py
+
+    .. note::
+        Since the dataset is large, there are available options to download only a subset of the data. For more information, run the following command in your terminal ::
+
+           python scripts/download_data.py --help
 
 CSV file
 --------