分水岭分割：无法拆分某些单元格

如何解决分水岭分割：无法拆分某些单元格

我试图分割细胞核的图像，但最终结果分割不足。似乎出现了一些大斑点，最好将其分解为较小的对象，例如右边缘的对象，请参见下文。

请问有什么我可以做的。我想使用分水岭分割（skimage或opencv）来分割这些大单元，例如右边缘中间的蓝色单元。

到目前为止，我的代码如下：

def segment_dapi(img_in):
    img = cv2.cvtColor(img_in,cv2.COLOR_BGR2GRAY)
    kernel = np.ones((3,3),np.uint8)

    # set the parameters
    thresh = 90
    min_size = 5


    # Adjust brightness
    lims = stretchlim(img)
    img_adj = imadjust(img,lims)

    # Threshold the image
    thres_val = np.percentile(img,thresh)

    _,bw_img = cv2.threshold(img_adj,thres_val,255,cv2.THRESH_BINARY)

    # Apply morphology opening to remove small objects
    img_obj = cv2.morphologyEx(bw_img,cv2.MORPH_OPEN,kernel,iterations=1)

    bg = cv2.dilate(img_obj,iterations=1)  # black points belong to the background

    # white points (value = 255) belong to the foreground
    dist_transform = cv2.distanceTransform(img_obj,cv2.DIST_L2,3)
    _,fg = cv2.threshold(dist_transform,min_size,cv2.THRESH_BINARY)
    fg = np.uint8(fg)
    fg_temp = 255/fg.max() * fg

    x = cv2.subtract(bg,fg)

    _,markers = cv2.connectedComponents(fg)
    markers = markers + 1  # prevent the markers from having values = 0
    markers[x == 255] = 0

    '''
    markers:
    > 1: absolute foreground
    = 1: absolute background
    = 0: unknown area (TBD by watershed)
    '''
    markers = cv2.watershed(img_in,markers)
    img_in[markers == -1] = [0,255]
    cv2.imwrite('watershed_borders.tif',img_in);
    small_img = cv2.resize(img_in,None,fx=1/2,fy=1/2)
    # cv2.imshow('Overlay',small_img)
    # cv2.waitKey(0)
    ''' 
    markers after watershed:
    = 0: background (set by watershed)
    = 1: background (because the markers have been shifted by 1)
    > 1: object labels 
    - 1: borders between object
    '''

    markers[markers>0] = markers[markers>0]-1
    markers[markers == -1] = 0

    print(markers.max())
    overlay = color.label2rgb(markers,bg_label=0)

    my_dpi = 72
    fig,ax = plt.subplots(figsize=(6000 / my_dpi,6000 / my_dpi),dpi=my_dpi)
    plt.imshow(overlay)
    ax.set_axis_off()
    plt.tight_layout()
    plt.show()



def stretchlim(img):
    nbins = 255
    tol_low = 0.01
    tol_high = 0.99
    sz = np.shape(img)
    if len(sz) == 2:
        img = img[:,:,None]
        sz = np.shape(img)

    p = sz[2]
    ilowhigh = np.zeros([2,p])
    for i in range(0,p):
        hist,bins = np.histogram(img[:,i].ravel(),nbins+1,[0,nbins])
        cdf = np.cumsum(hist) / sum(hist)
        ilow = np.argmax(cdf > tol_low)
        ihigh = np.argmax(cdf >= tol_high)
        if ilow == ihigh:
            ilowhigh[:,i] = np.array([1,nbins])
        else:
            ilowhigh[:,i] = np.array([ilow,ihigh])

    lims = ilowhigh / nbins
    return lims


def imadjust(img,lims):
    lims = lims.flatten()
    lowIn = lims[0]
    highIn = lims[1]
    lowOut = 0
    highOut = 1
    gamma = 1
    lut = adjustWithLUT(img,lowIn,highIn,lowOut,highOut,gamma)
    out = lut[img].astype(np.uint8)
    return out


def adjustWithLUT(img,gamma):
    lutLength = 256 # assumes uint8
    lut = np.linspace(0,1,lutLength)
    lut = adjustArray(lut,gamma)
    lut = img_as_ubyte(lut)
    return lut


def adjustArray(img,lIn,hIn,lOut,hOut,g):
    # %make sure img is in the range [lIn;hIn]
    img = np.maximum(lIn,np.minimum(hIn,img))
    out = ((img - lIn) / (hIn - lIn)) ** g
    out = out ** (hOut - lOut) + lOut
    return out

解决方法

您可能需要花费很长时间才能找到准确的参数来很好地进行分割，但是根据我的经验，它有些挑剔，因此对于要分割的下一张图片不起作用。这些天，我实际上会建议您使用经过训练的深度学习网络，例如cellpose。