Itcn Image J Download 11
Mathilda Gibby
ITCN is an ImageJ plugin for automatically counting the number cells within an image. The inputs are: (1) an estimation of the diameter of a cell, (2) an estimation of the minimum distance between cells, and (3) either a region of interest (ROI) selected with ImageJ's selection tools or a black and white mask image that is white in regions that are to be counted.
It seems to work on dark nuclei, not bright ones and perhaps you need to crop your images to remove the artificial background artefacts.
That being said, I agree with the advice given earlier: using a currently maintained plugin might save some headaches later on.
Hello, I have been using the Imagej plugin ITCN (IMage based tool for counting nuclei) and have found that it is excellent at finding and counting the nuclei I have imaged. Up until this point, my limited knowledge of cell profiler has limited the quality of my cell counting. What I would love to do would be to use this imagej plugin and combine it with the high throughput ability of cellprofiler. This would include getting the data from the imagej plugin and putting it into a spreadsheet just as IdentifyPrimaryObjects does. I have gone ahead and attached a sample hoechst stained image (note the high degree of clumping) as well as my current pipeline. Additionally, here is the link to the imagej plugin.
This pipeline is my approximation of what ITCN is doing, so it will need some more tweaking (for example, the threshold correction factor). The Crop module is included so I could test it out with a smaller image; feel free to remove it.
I have been working on a developmental biology project marking various nuclear markers along with a DAPI stain to determine percentage of marker expression. I have found that the ImageJ plugin ITCN ( ) works great for each marker when also using the CLAHE program. My problem is that I have around 6000 images to analyze and I would love to be able to automate the process. I have recorded a macro such as the following (which can itself be looped to individual image files) :
Compared to CT, MRI or contrast-enhanced MRI becomes the imaging modality of choice for diagnosis and treatment planning in the brain because of its sensitivity and superior image contrast in soft tissues. However, the multiplicity and complexity of the brain tumors under MRI often make tumor recognition and segmentation difficult for radiologists and other clinicians [4]. Consequently, automatic segmentation of heterogeneous tumors can greatly impact the clinical medicine by freeing physicians from the burden of the manual depiction of tumors. Furthermore, if computer algorithms can provide robust and quantitative measurements of tumor depiction, these automated measurements will greatly aid in the clinical management of brain tumors.
Recently, Shelhamer et al. [22] presented a novel FCN for semantic segmentation of natural scene images. This model can be trained in an end-to-end manner (also known as pixel-wise). Their results showed that the FCN outperformed the previous methods for semantic segmentation of a natural scene image in performance and speed. Inspired by the work in [22], we proposed a hybrid approach by constructing a deep cascaded neural network.
The starting point of the proposed system is in vivo MRI data consisting of four different sequences (FLAIR, T1, T1c, and T2), and the endpoint becomes a characterized tumor (see Figure 1). In the output image, a brain tumor is classified into four different zones: necrosis, edema, nonenhancing tumor, and enhancing tumor.
More specifically, the architecture of the proposed system includes an FCN followed by a CNN which accompanies small convolution kernels (see Figure 1). So the segmentation task based on this cascaded network can be divided into two major steps. In the first step, the pixel-wise FCN was used to quickly localize the tumor by marking the tumor region. Then, the patch-wise CNN was used to further categorize the above-identified tumor region into different subregions representing different pathologies. This system design was motivated and justified as follows. First, the FCN can take a whole image as the input and localization of a complete tumor only requires one-pass of the forward propagation. Thus, it can remarkably improve the segmentation efficiency. Second, this combination of FCN and CNN can alleviate the pixel sample class imbalance problem which is serious in MRI images. Thus, it can capture better segmentation details. Third, the intratumor characterization in the second step will only need to be applied to the tumor regions localized in the first step instead of the entire image, thereby significantly reducing forward computing time. Hereafter, the FCN and the CNN are referred as to tumor localization network (TLN) and intratumor classification network (ITCN), respectively.
We observed that a significant amount of low-level feature details such as location and edge could be lost after convolution striding and pooling. However, these lost features were valuable for semantic segmentation. Thus, two skip connections [22] were introduced for two purposes: (1) mitigating the loss of local image features and (2) combining local information obtained from intermediate layers (i.e., max pooling 4 and max pooling 3, resp.) with the global information in these deep layers (i.e., after 7 convolution layers). All relevant parameters used in the subnet TLN are shown in Table 3 below.
Given an image , is the intensity corresponding to the jth column at the ith row of . The data intensity normalization procedure is briefly described below:(1)Removed the top 1% and bottom 1% from each slice of the MRI data.(2)For each slice of MRI data , a normalized image was obtained. In the scaled image , each intensity value can be obtained as follows:where is the gray value of pixel prior to the normalization and and are the mean and standard deviation of the unscaled image , respectively.
The above-mentioned preprocessing method was used to process each modality MRI data including FLAIR, T1, T1c, and T2. Particularly, the FLAIR images were generated using fluid-attenuated inversion recovery protocol and useful in terms of differentiating the brain tumor from its normal background. Figure 4 presents some FLAIR slices before and after using the proposed image intensity normalization. We randomly selected 3 different cases from the FLAIR dataset. As shown in Figure 4 below, it is easy to find that the above-mentioned data normalization can improve the comparability of different slices.
Each feature map shown in Figures 1, 2, and 3 was associated with one convolution kernel. was computed as follows:where is the number of input channels, is a bias term, is an image from the rth input channel, and is the weight associated with the rth channel. In (2), denotes a convolution operator.
In the TLN, predictions were made for each pixel of the input image so that the loss function can be written as follows:where and is the pixel number of the input image. In every training, only one input image was used (the size of minibatch was 1).
The proposed system was compared with some other published methods. Those methods all have been validated on the BRATS 2015 dataset. A one-step segmentation method based on the FCN-8s was also implemented for the purpose of comparison. The FCN-8s can segment the input MRI images into 5 classes in a single step.
Also, the proposed system led to good details around boundaries. Figure 6 presents two representative examples of this observation. Since these brain tumors are complex, Figure 6 shows some good showcase examples. During the process, we found that the TLN subnet was able to effectively identify nearly all the tumor pixels. Subsequently, the ITCN subnet efficiently classified the tumor region into four subregions. Our method could largely detect the complete tumor and classify it to different tumor subregions from multimodality MRI images though there were a few misclassifications. This is not surprising because, pathologically, the brain glioma tumors invade their surrounding tissues rather than displacing them. Hence, the appearance of cancerous tissues and their surrounding (normal) tissues could be fairly similar under MRI.
Recently, we found that Pereira et al. [39] also proposed a hierarchical brain tumor segmentation approach from MRI HGG images. The difference between their method and our method is that they adopted the FCN in both first and second steps. We compared the results of our method with their method (see Table 7). Our proposed approach obtained the better DSC values (0.90, 0.81, and 0.81) in all tumor regions. Furthermore, the proposed method also yielded higher PPV values in the complete and enhancing tumor categories and a higher sensitivity in the core tumor category. Of note, Pereira et al. [39] trained and tested on the BRATS 2013 dataset but we on the BRATS 2015 dataset.
Based on quantitative and qualitative evaluations, we found that the proposed approach was able to accurately localize and segment complex brain tumors. We stipulate that there are two reasons. First, the ITCN subnet only represents and subsequently classifies the intratumoral region whereas other methods need to represent and classify all heterogeneous brain tissues. Second, intratumor subregions are usually very small proportions of the entire image. Other neural networks (e.g., FCN-8s) may suffer from the imbalance of different pixel labels. In the TLN subnet, our proposed method merged different tumor subregions into a whole tumor. Thus, the imbalance can be somewhat mitigated. In the ITCN subnet, we adopted the same quantity image patches of each class to train and optimize the model. In the future, deep learning neural networks could be expanded to include histological data and other data to further improve clinical management of brain cancers [40].
Comparison of two exemplary patients (patient 1: A-D, patient 2: E-H).A,E: Intraoperative cT1-scans. The biopsy location on this slice is marked by a white crosshair. B,F: Preoperative ADC-maps, which have been registered to intraoperative scans as described. The biopsy location on this slice is marked by a white crosshair. C,G: Scanned biopsy specimens of the respective location (HE stain, x20 magnification). D,H: semi-automatic cell counting on 8-bit images by the ImageJ plugin ITCN. Detected cells are marked with red dots. For patient 1(A-D), analysis yielded ADC = 658mm2/s and cellularity = 16840 cells/mm2. For patient 2 (E-H), it was ADC = 1479mm2/s and cellularity = 2208 cells/mm2.
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