Project: s41467-024-48107-5 | Function: Segment (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Iterative nucleus segmentation algorithm that applies different parameter combinations and uses AI-enhanced filtering to identify successful segmentations for 3D cell reconstruction

**KEY TERMS:** {
  "parameters": "segmentation parameter dictionary",
  "method": "segmentation algorithm type",
  "boxsize": "bounding box size constraints",
  "minthresh": "minimum intensity threshold",
  "min_distance": "minimum distance between peaks",
  "SVM": "trained classifier for segmentation validation"
}

**CONTEXT CODE:**             
class X_segment:
    def __init__(self,
                 centroid,segmentlabels
                 ):
        
        self.z,self.x,self.y = centroid
        """because the vertical segmentation is rotated from the persective of the horizontal segmentation we need to assign segments"""
        self.label     = []
        self.centroid  = (self.x,self.y,self.z)
        z,x,y = zip(*segmentlabels)     
      
        """the x-labels from segmentation"""
        for i in range((len(x))):
            self.label.append((x[i],y[i],z[i]))   
      


def PruneSegmentedOverlap(segmentations,rotation = 'x'):
    """this function takes the segmentation classes and clusters the bounding boxes
    together with a very wide margin of error so it might contain 3 stacked cells
    the goal is to find centroids and later align the segmentations with in the other directionsx"""
    boxcord,centroids,coordinates = [],[],{}
    
    """the nuclei segments and the overlap of the boxes"""
    
    for i in range(len(segmentations)):
        for j in range(len(segmentations)):
            if i != j:
                segmentations[i].IntersectedOverlap(segmentations[j])
            else:
                pass
        """the segment and the overlap that is priuned"""
        segment = segmentations[i] 
        segment.PruneOverlap()
    """return the boxcoordinates, the centroids and the coordinates"""
    popbin,structures = [],{}
    
    for i in segmentations.values():
        for n in i.overlap.keys():
            popbin.append(n)
        if i in popbin:
            pass
        else:
            if i.labelset != []:
                struct = Superstructure(i.labelset,i.centroids,i.boxes)
                structures[len(structures)] = Superstructure(i.labelset,i.centroids,i.boxes)
    return structures,segmentations


def Segment(
        #the z-stack coordinate, the image, do we store the images, the SVM model
        z,image,store,SVM,
        
        #the parameters that can be applied to the segmentaiton
        parameters = {},
        
        #show bbox
        show_bbox = False
        ):
    """This Function is called by the Segmentation Class again and again
    INPUTS:
        INT z
        ARR image (default 512x512, other size probably work)
        SVM model
        STRING: storepath where you want to display items      
        """
    
    raise NotImplementedError("Function has been masked for testing purposes")

class Segmentation:
    def __init__(self,
                 path,
                 array = [],parameters = [],
                 limit = 0,
                 demo_1 = False,demo_2 = False,
                 storepath = '',
                 magnification = 60,
                 store = True, AIenhanced = True, fit = True, SVM = True,segmentsides = False,show_bbox = False,
                 ):
        
        """We check if we have an image or a path and we check if we do AI enhanced segmentation"""
        if array == []:
            images = OpenImage(path)    
        else:
            images = array
        """raw data"""
        self.images = images
        
        """the segment sets in both directions"""
        self.segment_topview   = ''
        self.segment_sideview  = ''
        
        import os
        folder   = 'Segmentation\\'
        topview  = folder + 'Nucleiisegments topview\\'
        sideview = folder + 'Nucleiisegments sideview\\'
        

        """store the segmentations"""
        if store == True:
            split  = 'All\\' + path.split('.')[-2].split('\\')[-1]
            self.segment_sideview = 'C:\\Users\\hanse\\Desktop\\' + sideview + split
            try:
                os.mkdir(self.segment_sideview)
                print("segmentation folder created (sideview)")
            except:
                print("Segmentation folder exists or failed to find directory")
            split  = 'All\\' + path.split('.')[-2].split('\\')[-1]
            self.segment_topview = 'C:\\Users\\hanse\\Desktop\\' + topview + split + '\\'
            try:
                os.mkdir(self.segment_topview)
                print("segmentation folder created (horizontal)")
            except:
                print("Segmentation folder exists or failed to find directory")   
             
        SVM_topview   = False
        SVM_sideview  = False
        """the SVM that detects a succesfull segmnentation"""
        if AIenhanced == True:
            """import the classification algorithm"""
            from MLclassification import MLtrainer

            if segmentsides == True:
                """sideview trainer"""
                SVM_sideview = MLtrainer(folder = sideview + 'Training data\\')  
                SVM_sideview.SegmentationData()
                SVM_sideview.SVM_Classifier() 
                
            """topview trainer"""
            SVM_topview = MLtrainer(folder = topview + 'Training data\\')   
            SVM_topview.SegmentationData()
            SVM_topview.SVM_Classifier()  
            
            """store predictions"""
            self.classification = []

        """the limit of the image"""
        limit  = skimage.filters.threshold_otsu(numpy.array(images))
        """the distribution"""
        self.distribution = []
        """the coordinates"""
        self.signal  = []
        """new image set"""
        self.nuclei = numpy.zeros((len(images),len(images[0]),len(images[0][:,0])))
        for i in range(len(images)):
            for row in range(len(images[i])):
                for column in range(len(images[i][:,0])):
                    self.distribution.append(copy.copy(images[i][row][column]))
                    


Complete the function implementation replacing "raise NotImplementedError".