Added implant prediction for cranioplasty. (#843)

.github/workflows/bundles.yml

@@ -82,6 +82,20 @@ jobs:
           url: "https://raw.githubusercontent.com/invesalius/weights/main/mandible_ct/mandible_jit_ct.pt"
           target: ./ai/mandible_jit_ct/
 
+      - name: Download cranioplasty binary ai weight
+        if: ${{ matrix.ai_ready == 1 }}
+        uses: suisei-cn/actions-download-file@818d6b7dc8fe73f2f924b6241f2b1134ca1377d9 
+        with:
+          url: "https://github.com/invesalius/weights/blob/main/cranioplasty_jit_ct_binary/cranioplasty_jit_ct_binary.pt"
+          target: ./ai/cranioplasty_jit_ct_binary/
+
+      - name: Download cranioplasty gray ai weight
+        if: ${{ matrix.ai_ready == 1 }}
+        uses: suisei-cn/actions-download-file@818d6b7dc8fe73f2f924b6241f2b1134ca1377d9 
+        with:
+          url: "https://github.com/invesalius/weights/blob/main/cranioplasty_jit_ct_gray/cranioplasty_jit_ct_gray.pt"
+          target: ./ai/cranioplasty_jit_ct_gray/
+
       - name: Download trachea ai weight
         if: ${{ matrix.ai_ready == 1 }}
         uses: suisei-cn/actions-download-file@818d6b7dc8fe73f2f924b6241f2b1134ca1377d9 

bundle_tools/win/insert_version_date.py

@@ -19,40 +19,53 @@
 
 # -*- coding: UTF-8 -*-
 
+import pathlib
 import sys
-import pathlib 
+
 
 def edit_file(path, date, commit_hash, nightly):
     """
-    This code inserts the date and hash of the last commit into dialogs.py so that 
+    This code inserts the date and hash of the last commit into dialogs.py so that
     the user can provide this information (about window) in any support request.
     """
 
-    file = open(path,"r")
+    file = open(path, "r")
     content = file.read()
     file.close()
 
-    #path = invesalius/gui/dialog.py
+    # path = invesalius/gui/dialog.py
     to_replace = "info.Version = const.INVESALIUS_VERSION"
 
     if nightly:
-        new_content = to_replace + " + '(Nightly)' + '\\n' + 'Release date: '+ '"\
-                                 + date + "'+'\\n Commit hash: " + commit_hash[0:9] + "...'"
+        new_content = (
+            to_replace
+            + " + '(Nightly)' + '\\n' + 'Release date: '+ '"
+            + date
+            + "'+'\\n Commit hash: "
+            + commit_hash[0:9]
+            + "...'"
+        )
     else:
-        new_content = to_replace + "+ '\\n' + 'Release date: '+ '"\
-                                 + date + "'+'\\n Commit hash: " + commit_hash[0:9] + "...'"
-
-
+        new_content = (
+            to_replace
+            + "+ '\\n' + 'Release date: '+ '"
+            + date
+            + "'+'\\n Commit hash: "
+            + commit_hash[0:9]
+            + "...'"
+        )
+
     content = content.replace(to_replace, new_content)
 
-    file = open(path,"w")
+    file = open(path, "w")
     file.write(content)
     file.close()
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     path = pathlib.Path(sys.argv[1])
     date = sys.argv[2]
     commit_hash = sys.argv[3]
     nightly = sys.argv[4]
 
-    edit_file(path, date, commit_hash, bool(nightly))
+    edit_file(path, date, commit_hash, bool(nightly))

docs/devel/example_closure.py

@@ -1,15 +1,16 @@
 # Python closure example
 
+
 def OuterCount(start):
-     counter = [start]  # counter is 1-element array
-     print("Passed outside")
+    counter = [start]  # counter is 1-element array
+    print("Passed outside")
 
-     def InnerCount():
-         counter[0] = counter[0] + 1
-         print("Passed inside")
-         return counter[0]
+    def InnerCount():
+        counter[0] = counter[0] + 1
+        print("Passed inside")
+        return counter[0]
 
-     return InnerCount
+    return InnerCount
 
 
 print("Init counter at 5")

docs/devel/example_singleton.py

@@ -4,13 +4,14 @@
 # http://en.wikipedia.org/wiki/Singleton_pattern
 # http://www.vincehuston.org/dp/singleton.html
 
+
 class Singleton(type):
     # This is a Gary Robinson implementation:
     # http://www.garyrobinson.net/2004/03/python_singleto.html
-    def __init__(cls,name,bases,dic):
-        super(Singleton,cls).__init__(name, bases, dic)
+    def __init__(cls, name, bases, dic):
+        super(Singleton, cls).__init__(name, bases, dic)
         cls.instance = None
-        
+
     def __call__(cls, *args, **kw):
         if cls.instance is None:
             cls.instance = super(Singleton, cls).__call__(*args, **kw)
@@ -21,21 +22,21 @@ class Bone(object):
     # Only one project will be initialized per time. Therefore, we use
     # Singleton design pattern for implementing it
     __metaclass__ = Singleton
-    
+
     def __init__(self):
         self.size = 100
-        
+
     def RemovePart(self, part_size):
         self.size -= part_size  # self.size = self.size - part_size
 
 
-class Dog():
+class Dog:
     def __init__(self, name):
         self.name = name
         self.bone = Bone()
-        
+
     def EatBonePart(self, part_size):
-        self.bone.RemovePart(part_size) 
+        self.bone.RemovePart(part_size)
 
 
 print("Initial state:")
@@ -53,5 +54,3 @@ def EatBonePart(self, part_size):
 d2.EatBonePart(20)
 print(f"Bone size of {d1.name}: {d1.bone.size}")
 print(f"Bone size of {d2.name}: {d2.bone.size}")
-
-

docs/devel/example_singleton_pubsub.py

@@ -27,7 +27,7 @@ def __init__(self):
         self.__bind_events()
 
     def __bind_events(self):
-        Publisher.subscribe(self.RemovePiece, 'Eat piece of pizza')
+        Publisher.subscribe(self.RemovePiece, "Eat piece of pizza")
 
     def RemovePiece(self, pubsub_evt):
         person = pubsub_evt
@@ -38,13 +38,13 @@ def RemovePiece(self, pubsub_evt):
             print(f"{person.name} is hungry!")
 
 
-class Person():
+class Person:
     def __init__(self, name):
         self.name = name
         self.pizza = Pizza()
 
     def EatPieceOfPizza(self):
-        Publisher.sendMessage('Eat piece of pizza', pubsub_evt=self)
+        Publisher.sendMessage("Eat piece of pizza", pubsub_evt=self)
 
 
 print("Initial state:")

invesalius/constants.py

@@ -613,6 +613,7 @@
 ID_SEGMENTATION_BRAIN = wx.NewIdRef()
 ID_SEGMENTATION_TRACHEA = wx.NewIdRef()
 ID_SEGMENTATION_MANDIBLE_CT = wx.NewIdRef()
+ID_PLANNING_CRANIOPLASTY = wx.NewIdRef()
 ID_CROP_MASK = wx.NewIdRef()
 ID_DENSITY_MEASURE = wx.NewIdRef()
 ID_MASK_DENSITY_MEASURE = wx.NewIdRef()
@@ -734,6 +735,11 @@
 SURFACE_INTERPOLATION = 1
 LANGUAGE = 2
 SLICE_INTERPOLATION = 3
+# Logging
+LOGGING = 4
+LOGGING_LEVEL = 5
+APPEND_LOG_FILE = 6
+LOGFILE = 7
 
 
 # ------------ Logging options key------------

invesalius/data/imagedata_utils.py

@@ -27,6 +27,7 @@
 import numpy as np
 from scipy.ndimage import shift, zoom
 from skimage.color import rgb2gray
+from skimage.measure import label
 from vtkmodules.util import numpy_support
 from vtkmodules.vtkFiltersCore import vtkImageAppend
 from vtkmodules.vtkImagingCore import vtkExtractVOI, vtkImageClip, vtkImageResample
@@ -574,6 +575,19 @@ def get_LUT_value(data: np.ndarray, window: int, level: int) -> np.ndarray:
     return data
 
 
+def get_LUT_value_normalized(img, a_min, a_max, b_min=0.0, b_max=1.0, clip=True):
+    # based on https://docs.monai.io/en/latest/_modules/monai/transforms/intensity/array.html#ScaleIntensity
+
+    print(a_min, a_max, b_min, b_max, clip)
+    img = (img - a_min) / (a_max - a_min)
+    img = img * (b_max - b_min) + b_min
+
+    if clip:
+        img = np.clip(img, b_min, b_max)
+
+    return img
+
+
 def image_normalize(image, min_=0.0, max_=1.0, output_dtype=np.int16):
     output = np.empty(shape=image.shape, dtype=output_dtype)
     imin, imax = image.min(), image.max()
@@ -706,3 +720,10 @@ def random_sample_sphere(radius=3, size=100):
     scale = radius * np.divide(r, norm)
     xyz = scale * uvw
     return xyz
+
+
+def get_largest_connected_component(image):
+    labels = label(image)
+    assert labels.max() != 0
+    largest_component = labels == np.argmax(np.bincount(labels.flat)[1:]) + 1
+    return largest_component

invesalius/gui/deep_learning_seg_dialog.py

@@ -533,3 +533,128 @@ def OnSegment(self, evt):
     def OnStop(self, evt):
         super().OnStop(evt)
         self.chk_apply_resize_by_spacing.Enable()
+
+
+class ImplantSegmenterDialog(DeepLearningSegmenterDialog):
+    def __init__(self, parent):
+        super().__init__(
+            parent=parent,
+            title=_("Implant prediction (CT)"),
+            has_torch=True,
+            has_plaidml=False,
+            has_theano=False,
+            segmenter=segment.ImplantCTSegmentProcess,
+        )
+
+    def _init_gui(self):
+        super()._init_gui()
+
+        self.patch_txt = wx.StaticText(self, label="Patch size:")
+
+        patch_size = [
+            "48",
+            "96",
+            "160",
+            "192",
+            "240",
+            "288",
+            "320",
+            "336",
+            "384",
+            "432",
+            "480",
+            "528",
+        ]
+
+        self.patch_cmb = wx.ComboBox(self, choices=patch_size, value="480")
+
+        self.path_sizer = wx.BoxSizer(wx.HORIZONTAL)
+        self.path_sizer.Add(self.patch_txt, 0, wx.EXPAND | wx.ALL, 5)
+        self.path_sizer.Add(self.patch_cmb, 2, wx.EXPAND | wx.ALL, 5)
+
+        self.method = wx.RadioBox(
+            self,
+            -1,
+            "Method:",
+            wx.DefaultPosition,
+            wx.DefaultSize,
+            ["Binary", "Gray"],
+            2,
+            wx.HORIZONTAL | wx.ALIGN_LEFT | wx.NO_BORDER,
+        )
+
+        self.method_sizer = wx.BoxSizer(wx.HORIZONTAL)
+        self.method_sizer.Add(self.method, 2, wx.ALL, 5)
+
+        self.Layout()
+        self.Centre()
+
+    def _do_layout(self):
+        super()._do_layout()
+        self.main_sizer.Insert(8, self.path_sizer, 0, wx.EXPAND | wx.ALL, 5)
+        self.main_sizer.Insert(9, self.method_sizer, 0, wx.EXPAND | wx.ALL, 5)
+
+    def OnSegment(self, evt):
+        self.ShowProgress()
+        self.t0 = time.time()
+        self.elapsed_time_timer.Start(1000)
+        image = slc.Slice().matrix
+        backend = self.cb_backends.GetValue()
+        if backend.lower() == "pytorch":
+            try:
+                device_id = self.torch_devices[self.cb_devices.GetValue()]
+            except (KeyError, AttributeError):
+                device_id = "cpu"
+        else:
+            try:
+                device_id = self.plaidml_devices[self.cb_devices.GetValue()]
+            except (KeyError, AttributeError):
+                device_id = "llvm_cpu.0"
+        apply_wwwl = self.chk_apply_wwwl.GetValue()
+        create_new_mask = self.chk_new_mask.GetValue()
+        use_gpu = self.chk_use_gpu.GetValue()
+        prob_threshold = self.sld_threshold.GetValue() / 100.0
+        method = self.method.GetSelection()
+
+        self.btn_close.Disable()
+        self.btn_stop.Enable()
+        self.btn_segment.Disable()
+        self.chk_new_mask.Disable()
+
+        window_width = slc.Slice().window_width
+        window_level = slc.Slice().window_level
+
+        overlap = self.overlap_options[self.overlap.GetSelection()]
+
+        patch_size = int(self.patch_cmb.GetValue())
+
+        try:
+            self.ps = self.segmenter(
+                image,
+                create_new_mask,
+                backend,
+                device_id,
+                use_gpu,
+                overlap,
+                apply_wwwl,
+                window_width,
+                window_level,
+                method=method,
+                patch_size=patch_size,
+                resize_by_spacing=True,
+                image_spacing=slc.Slice().spacing,
+            )
+            self.ps.start()
+        except (multiprocessing.ProcessError, OSError, ValueError) as err:
+            self.OnStop(None)
+            self.HideProgress()
+            dlg = dialogs.ErrorMessageBox(
+                None,
+                "It was not possible to start brain segmentation because:" + "\n" + str(err),
+                "Brain segmentation error",
+                #  wx.ICON_ERROR | wx.OK,
+            )
+            dlg.ShowModal()
+
+    def OnStop(self, evt):
+        super().OnStop(evt)