CT Imaging Of Tumors Research Articles

CT and PET radiomic features associated with major pathologic response to neoadjuvant immunotherapy in early-stage non-small cell lung cancer (NSCLC).

9031 Background: An early biomarker of response to immunotherapy (IO) is needed urgently to identify the patients (pts) who will derive benefit. We reported the first clinical trial of neoadjuvant IO (nIO) in resectable non-small cell lung cancer (NSCLC) (NCT02259621). In this study, we investigated whether there was an association between MPR and radiomic features (RF) in [18F]-fluorodeoxyglucose ([18F]-FDG) PET and standard CT images obtained at baseline and after nIO in early stage NSCLC tumors. Methods: Prior to receiving neoadjuvant nivolumab or nivolumab/ipilimumab, patients with Stage I-IIIA NSCLC underwent two [18F]-FDG PET-CTs and/or plain CTs: a baseline scan at enrollment (PRE), and after nIO (POST). After neoadjuvant treatment, tumors were resected and evaluated for MPR. Volumes of interest (VOIs) were drawn around primary tumors on the scans. Using our novel radiomic software, Imager-4D, VOIs were evaluated for 20 RFs assessing [18F]-FDG standard uptake value (SUV) or Hounsfield unit (HU) heterogeneity and spatial distribution in PET and CT images respectively. The baseline, post-treatment, and percent change in RFs before and after nIO were compared between tumors with and without MPR. Wilcoxon test was used for the comparisons. Results: The PRE and POST scans of 24 pts were analyzed. All pts had PRE and POST CTs performed, and 17 pts had PRE and POST [18F]-FDG PET-CT scans. 7 of 24 (29%) had MPR. In the CT scan analysis, HU-based RFs of voxel count, total volume, energy, entropy, homogeneity, contrast, and dissimilarity in POST CT scans each significantly association with MPR. In the PET scan analysis, SUV mean and voxel count RFs in the POST scans, and the percent change in the cluster shade RF between PRE and POST scans were significantly associated with MPR. Conclusions: Collectively, we identified a significant increase in heterogeneity in the POST CT images of NSCLC tumors that had MPR. This association may reflect increased T cell infiltration or tumor necrosis. In contrast, most [18F]-FDG-based RFs did not distinguish MPR vs non-MPR tumors, although the sample size was limited. We will further investigate these HU-based RFs as non-invasive markers of response to IO in conjunction with pathologic markers of IO response and in a larger patient cohort.

SERU: A cascaded SE‐ResNeXT U‐Net for kidney and tumor segmentation

SummaryAccording to statistics, kidney cancer is one of the most deadly cancer. An early and accurate diagnosis can significantly increase the cure rate. Accurate segmentation of kidney tumors in CT images plays an important role in kidney cancer diagnosis. However, it is a challenging task due to many different aspects, such as low contrast, irregular motion, diverse shapes, and sizes. For solving this issue, we proposed a SE‐ResNeXT U‐Net (SERU) model in this study, which takes the advantages of SE‐Net, ResNeXT and U‐Net. Besides, we implement our model in a coarse‐to‐fine manner to utilize the information of context and key slices from the left and right kidney. We train and test our method on the KiTS19 Challenge. Experimental results demonstrate that our model can achieve promising results.

Protein sulfenic acid-mediated anchoring of gold nanoparticles for enhanced CT imaging and radiotherapy of tumors in vivo.

Radiotherapy (RT) has become one of the most widely used treatments for malignant tumors in clinics. Developing a novel radiosensitizer for the integration of precise diagnosis and effective radiotherapy against hypoxic tumors is desirable but remains a great challenge. Herein, protein sulfenic acid reactive gold nanoparticles as effective radiosensitizers were for the first time reported for enhanced X-ray computed tomography (CT) imaging and radiotherapy of tumors in vivo. The gold nanoparticles were decorated with biocompatible poly(ethylene glycol), folic acid (FA), and sulfenic acid reactive groups 1,3-cyclohexanedione (CHD). Such a nanostructure enables on-site immobilization within tumors under oxidative stress through the specific reaction between CHD and endogenous protein sulfenic acids resulting in enhanced accumulation and retention of gold nanoparticles within tumors, which remarkably improves the sensitivity of CT imaging and the radiotherapeutic efficacy of tumors in living mice. This study thus is the first to demonstrate that protein sulfenic acid reactive gold nanoparticles with a tumor anchoring function may serve as effective radiosensitizers for clinical X-ray theranostic application in the future.

Review of deep learning segmentation methods for CT images of liver tumors

Review of deep learning segmentation methods for CT images of liver tumors

Empirical Comparisons of Deep Learning Networks on Liver Segmentation

Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases. In recent years, due to the great improvement of hard device, many deep learning based methods have been proposed for automatic liver segmentation. Among them, there are the plain neural network headed by FCN and the residual neural network headed by Resnet, both of which have many variations. They have achieved certain achievements in medical image segmentation. In this paper, we firstly select five representative structures, i.e., FCN, U-Net, Segnet, Resnet and Densenet, to investigate their performance on liver segmentation. Since original Resnet and Densenet could not perform image segmentation directly, we make some adjustments for them to perform live segmentation. Our experimental results show that Densenet performs the best on liver segmentation, followed by Resnet. Both perform much better than Segnet, U-Net, and FCN. Among Segnet, U-Net, and FCN, U-Net performs the best, followed by Segnet. FCN performs the worst.

Automatic Diagnosis of Liver Tumor in Ct Images

Medical Image Segmentation is the important tool for diagnosing tumor and for planning how to do treatment. The intention of this study is to detect tumor from CT liver images. Initially, liver is segmented from abdomen CT images. Then SVM Classification is included to classify the normal and abnormal liver structure. If it is abnormal then the tumor will be segmented from liver structure. This technique is computed using sensitivity, specificity and accuracy and is providing good result.

Radiosensitization and micro CT imaging of multifunctional gold nanoparticles in lung adenocarcinoma A549 cell: an in vivo animal study

Objective To evaluate the radiosensitization effect and micro CT imaging of multifunctional gold nanoparticles in lung adenocarcinoma A549 tumor-bearing mouse models. Methods The tumor-bearing mice were injected with gold nanoparticles and irradiated with different energy levels of 160 kV and 6 MV X-ray. The tumor volume changes were measured. Intra-tumoral injection of gold nanoparticles was administered and micro CT scan was performed at different time points to observe the imaging and retention time of gold nanoparticles in the tumor tissues. Results The tumor volume did not significantly differ between the control and gold nanoparticles groups (P=0.941). The tumor volume in the 6 MV X-ray combined with gold nanoparticles group was slightly reduced compared with that in the 6 MV X-ray group with no statistical significance (P=0.730). The tumor volume in the 160 kV X-ray combined with gold nanoparticles group was significantly smaller than that in the 160 kV X-ray group (P=0.026). Micro CT scan demonstrated that gold nanoparticles could be deposited in the tumors for 30 d and yielded excellent imaging effect. No gold nanoparticles-induced toxicity was observed. Conclusions Multifunctional gold nanoparticles exert significant radiosensitization effect in the lung adenocarcinoma A549 transplanted tumors irradiated with 160 kV X-ray. Stable CT imaging of the gold nanoparticles-injected tumors can be used as a potential method for mapping and delineating the target area in tumor-guided radiotherapy. Key words: Multifunctional gold nanoparticle; Lung adenocarcinoma; Tumor-bearing mouse; Radiosensitization

Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor Segmentation in CT Images.

Due to the unpredictable location, fuzzy texture and diverse shape, accurate segmentation of the kidney tumor in CT images is an important yet challenging task. To this end, we in this paper present a cascaded trainable segmentation model termed as Crossbar-Net. Our method combines two novel schemes: (1) we originally proposed the crossbar patches, which consists of two orthogonal non-squared patches (i.e., the vertical patch and horizontal patch). The crossbar patches are able to capture both the global and local appearance information of the kidney tumors from both the vertical and horizontal directions simultaneously. (2) With the obtained crossbar patches, we iteratively train two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded training manner. During the training, the trained sub-models are encouraged to become more focus on the difficult parts of the tumor automatically (i.e., mis-segmented regions). Specifically, the vertical (horizontal) sub-model is required to help segment the mis-segmented regions for the horizontal (vertical) sub-model. Thus, the two sub-models could complement each other to achieve the self-improvement until convergence. In the experiment, we evaluate our method on a real CT kidney tumor dataset which is collected from 94 different patients including 3,500 CT slices. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior performance of our method on the Dice similarity coefficient, true positive fraction, centroid distance and Hausdorff distance. Moreover, to exploit the generalization to other segmentation tasks, we also extend our Crossbar-Net to two related segmentation tasks: (1) cardiac segmentation in MR images and (2) breast mass segmentation in X-ray images, showing the promising results for these two tasks. Our implementation is released at https: //github.com/Qianyu1226/Crossbar-Net.

One-pot bottom-up fabrication of biocompatible PEGylated WS2 nanoparticles for CT-guided photothermal therapy of tumors in vivo

BackgroundTungsten disulfide (WS2), which enjoyed a good potential to be a promising clinical theranostic agent for cancer treatment, is still subject to the tedious synthesis procedure. MethodsHere, we reported a one-pot ‘bottom-up’ hydrothermal strategy for the fabrication of PEGylated WS2 nanoparticles (NPs). The WS2-PEG nanoparticles were characterized systematically. The CT imaging and photothermal therapy against tumor as well as biosafety in vitro and in vivo were also investigated. ResultsThe obtained WS2-PEG NPs enjoyed obvious merits of good solubility and favorable photothermal performance. WS2-PEG NPs exhibited desirable photothermal ablation ability against cancer cells and cancer cell-bearing mice in vitro and in vivo. MTT assay and histological analysis demonstrated the low cytotoxicity and biotoxicity of WS2-PEG NPs, providing a valid biosafety guarantee for the coming biomedical applications. In addition, thanks to the obvious X-ray attenuation of W atom, the WS2-PEG NPs can also be served as a favorable contrast agent for CT imaging of tumors. ConclusionWS2-PEG NPs has enjoyed a good potential to be a promising clinical CT-guided photothermal therapeutic agent against cancers.

Cyclic RGD-Functionalized and Disulfide-Crosslinked Iodine-Rich Polymersomes as a Robust and Smart Theranostic Agent for Targeted CT Imaging and Chemotherapy of Tumor.

There is tremendous interest in integrating CT imaging with chemotherapy; however, reported iodine-based nanosystems such as nanogels and nano-emulsions display typically reduced contrast coefficient, low drug loading and stability, and poor targetability. Here, cRGD-functionalized disulfide-crosslinked iodine-rich polymersomes (cRGD-XIPs) were designed as a novel, robust and smart theranostic agent and investigated for targeted CT imaging and chemotherapy of malignant tumors.Methods: cRGD-XIPs were prepared from co-self-assembly of poly(ethylene glycol)-b-poly(dithiolane trimethylene carbonate-co-iodinated trimethylene carbonate) (PEG-P(DTC-IC)) and cRGD-PEG-P(DTC-IC) block copolymers. In vitro and in vivo CT contrast effect of cRGD-XIPs was studied using αvβ3-overexpressing B16 melanoma as a tumor model in comparison with clinical agent iohexol. The therapeutic efficacy of doxorubicin-loaded cRGD-XIPs (cRGD-XIPs-Dox) to B16 melanoma was investigated and compared with XIPs-Dox (non-targeted), cRGD-IPs-Dox (non-crosslinked) and free Dox.Results: cRGD-XIPs were formed with 55.5 wt.% iodine and ca. 90 nm in diameter. cRGD-XIPs-Dox with a Dox loading of 15.3 wt.% bared superior colloidal stability and reduction-responsive drug release. Notably, blank cRGD-XIPs showed a maximum-tolerated dose (MTD) > 400 mg iodine equiv./kg while cRGD-XIPs-Dox had an MTD > 150 mg Dox equiv./kg, ca. 15-fold improvement over free Dox. cRGD-XIPs revealed superior CT contrast effect and achieved 46.5- and 24.0-fold better enhancement of CT imaging of B16 melanoma than iohexol at 4 h following intratumoral and intravenous injection, respectively. cRGD-XIPs-Dox displayed an elimination half-life of 6.5 h and an elevated accumulation of 6.68% ID/g in the tumors. Furthermore, cRGD-XIPs-Dox was significantly more effective than XIPs-Dox and cRGD-XPs-Dox in inhibiting growth of B16 melanoma model.Conclusion: This proof-of-concept study demonstrates that cRGD-XIPs are a robust, non-toxic and smart polymeric theranostic agent that can not only significantly enhance CT imaging of tumors but also mediate efficient tumor-targeted chemotherapy. XIPs offer a unique and safe platform for theranostic polymersomes that pre-select patients using CT imaging prior to targeted chemotherapy with the same system.

Research on Feature Extraction of Tumor Image Based on Convolutional Neural Network

Medical images play a very important role in making the right diagnosis for the doctor and in the patient’s treatment process. Using intelligent algorithms makes it possible to quickly distinguish the lesions of medical images, and it is especially important to extract features from images. Many studies have integrated various algorithms into medical images. For medical image feature extraction, a large amount of data is analyzed to obtain processing results, helping doctors to make more accurate case diagnosis. In view of this, this paper takes tumor images as the research object, and first performs local binary pattern feature extraction of the tumor image by rotation invariance. As the image shifts and the rotation changes, the image is stationary relative to the coordinate system. The method can accurately describe the texture features of the shallow layer of the tumor image, thereby enhancing the robustness of the image region description. Focusing on image feature extraction based on convolutional neural network (CNN), the basic framework of CNN is built. In order to break the limitations of machine vision and human vision, the research is extended to multi-channel input CNN for image feature extraction. Two convolution models of Xception and Dense Net are built to improve the accuracy of the CNN algorithm. It can be seen from the experimental results that the CNN algorithm shows high accuracy in tumor image feature extraction. In this paper, the CNN algorithm is compared with several classical algorithms in the local binary mode. The CNN algorithm has more accurate feature extraction ability for tumor CT images on a larger data basis. Furthermore, the advantages of CNN algorithms in this field are demonstrated.

Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model

Lung cancer is the most common leading cancer in both men and women all over the world. Accurate image segmentation is an essential image analysis tool that is responsible for partitioning an image into several sub-regions. Active contour model have been widely used for effective image segmentation methods as this model produce sub-regions with continuous boundaries. It is used in the applications such as image analysis, deep learning, computer vision and machine learning. Advanced level set method helps to implement active contours for image segmentation with good boundary detection accuracy. This paper proposes a model based on active contour using level set methods for segmentation of such lung CT images and focusing 3D lesion refinement. The features were determined by applying a multi-scale Gaussian filter. This proposed method is able to detect lung tumors in CT images with intensity, homogeneity and noise. The proposed method uses LIDC-IDRI dataset images to segment accurate 3D lesion of lung tumor CT images.

AB019. LA04. Approach to the prevascular mass

: A wide variety of lesions can present as a localized tumor or mass in the mediastinum. The radiological evaluation of these patients begins with chest radiograph and is followed by CT or MRI. CT and MR imaging allow visualization of the exact location of the lesions and, in some cases, identification of the structures from which they arise. The International Thymic Malignancy Interest Group (ITMIG) has introduced a new definition of mediastinal compartments to be used with cross-sectional imaging and adopted as a new standard. This clinical classification defines 3-compartment model of prevascular (anterior), a visceral (middle), and a paravertebral (posterior) compartment, with anatomic boundaries defined clearly by computed tomography. The most common masses in the prevascular compartment include thymic abnormalities (cysts, hyperplasia, thymoma, thymic carcinoma, and neuroendocrine tumors), germ cell neoplasms, lymphoma; metastatic lymphadenopathy, and intrathoracic goiter. Thymoma is classified into non-invasive and invasive thymoma clinically, and is divided into 5 subtypes (type A, AB, B1, B2, and B3) based on the WHO classification histologically. The 4th edition of the WHO classification is the latest and it was published in 2015. Thymic carcinoma is completely different from thymoma histologically, and is more aggressive than thymoma. Thymic neuroendocrine tumors are divided into well differentiated neuroendocrine carcinomas (carcinoid tumor) and poorly differentiated neuroendocrine carcinomas which consist of large cell neuroendocrine carcinoma and small cell carcinoma. Mature teratoma is usually seen as a mass containing soft-tissue, fluid, fat, or calcium attenuation, or any combination of the four. Malignant germ cell tumors are divided into seminoma and non-seminomatous tumors. This tumor manifests as a large homogeneous soft tissue mass and mimics lymphoma with nodal coalescence. Anterior mediastinal cysts are thymic cyst and pericardial cyst, etc. Simple cyst appears as well-defined water-attenuation mass with imperceptible wall. At first, I would like to show the new definition of mediastinal compartments which is useful for making a differential diagnosis in my lecture. Then, I will demonstrate CT and MR images of prevascular mediastinal tumors, and talk how to differentiate and diagnose these entities.

Targeted detection of the cancer cells using the anti-CD24 bio modified PEGylated gold nanoparticles: the application of CD24 as a vital cancer biomarker

Objective(s): The central role of molecular imaging modalities in cancer management is an undeniable fact that could help to diagnose cancer tumors in early stages. The main aim of this study is to prepare a novel targeted molecular imaging nanoprobe of CD24-PEGylated Au NPs to improve the ability of Computed tomography scanning (CT scan) outputs for both in vitro and in vivo detection of breast cancer (4T1) cells. Materials and methods: Gold nanoparticles (Au NPs) were synthesized and coated with polyethylene glycol (PEG) chains in order to improve the stability of the Au NPs and to provide bio modification points for antibody immobilization. The synthesized nanoprobe was used for both in vitro and in vivo targeted CT imaging breast cancer cells (4T1) and the xenograft tumor model. Results: Findings showed that the attenuation coefficient of 4T1 cells that were targeted by CD24-PEGylated Au NPs is calculated to be over two times higher than the untargeted 4T1 cells (15 HU vs 39 HU, respectively). Indeed, the results clearly reveal that the developed CD24-PEGylated Au NPs showed the tumor CT enhancement was higher than that of Omnipaqe which used as control. Also, the CT number values of the tumor area at different time points gradually increased after 60 min post injection and was significantly higher than before injection. Conclusions: Results showed the introduced CT imaging nanoprobe (Au NPs-PEGylated) could be useful for CT imaging of breast tumors under in vivo condition. Overall, it is concluded that Au NPs-PEGylated contrast media is able to detect breast cancer (4T1) cells and is more effective compared with other casual methods.

Thiol-capped Bi nanoparticles as stable and all-in-one type theranostic nanoagents for tumor imaging and thermoradiotherapy

Bismuth (Bi)-based semiconductors and composites have been well developed for cancer treatments due to their multimodal diagnostic and therapeutic functions, while the development of metallic Bi nanocrystals is rather hindered by the easy-oxidation and unsatisfactory near-infrared photoabsorption. Herein, we prepared uniform Bi nanoparticles (∼40 nm) capped with thiol ligands (Bi-SR) through the chemical reduction method and then surface-modified them with PEGylated phospholipids. The resulting Bi-SR-PEG has strong NIR absorbance and high photothermal conversion efficiency of 45.3%. Importantly, thiol ligands on the surface of Bi-SR-PEG can significantly prevent the metal Bi core from oxidation because of the strong chemisorption energy between sulfur and metal, thus maintaining the high stability and long-term near-infrared photoabsorption. More importantly, given the low toxicity, good blood compatibility and high X-ray attenuation coefficient, Bi-SR-PEG can passively accumulatein the tumor area through intravenous injection, endowing them with the simultaneous tumor CT imaging and thermoradiotherapy, and thereafter they can be metabolized and excreted from the mice body overtime. Therefore, the satisfying therapeutic effect of tumors can be achieved, undoubtedly verifying that Bi-SR-PEG can be used as a novel, stable and all-in-one type theranostic nanoagent for cancer treatment.

Transhepatic arterial embolization with superparamagnetic iron oxide and lipiodol for the treatment of VX2 tumor in rabbits

To evaluate the feasibility and therapeutic efficacy of transhepatic arterial embolization with superparamagnetic iron oxide (SPIO) and lipiodol (LIP) for the treatment of VX2 tumor in rabbits. Methods: Twenty-four rabbits with hepatic VX2 tumors by surgical implantation were randomly divided into 4 groups and treated with transhepatic arterial embolization of 4 different agents as follows (n=6 each): doxorubicin (DOX) group, DOX-LIP group, SPIO-DOX group, and SPIO-DOX-LIP group. Liver function (AST and ALT) was measured at 0, 1, 3, 5 and 7 d after transhepatic arterial embolization. The serum DOX level was measured at 0, 5, 15, 30, 60, and 120 minutes after transhepatic arterial embolization. MRI was performed at 7 d after the treatment to assess the distribution of SPIO in the SPIO-DOX group and SPIO-DOX-LIP group, while CT was performed to assess the distribution of LIP in the DOX-LIP group and SPIO-DOX-LIP group. All the rabbits were sacrificed and their livers were removed at 7 d after treatment for the detection of tissue DOX level. The histopathologic examinations were performed including HE staining, Prussian blue staining and TUNEL assay, and then the tumor necrosis percentage and apoptosis index were calculated. Results: Compared to the DOX group, the levels of AST and ALT in other 3 groups were significantly elevated at 1 and 3 d after embolization (P<0.05). The levels of ALT and AST in the DOX group, DOX-LIP group or SPIO-DOX-LIP group returned to the baseline at day 7, there were no significant differences (P>0.05). The SPIO-DOX-LIP group exhibited the lowest serum DOX level at all time points up to 120 minutes after embolization (P<0.05). However, the tissue DOX level in the SPIO-DOX-LIP group was the highest among all groups at day 7 (P<0.05). The SPIO-DOX group and SPIO-DOX-LIP group showed significantly lower MRI signal intensity of tumors in T2 weighted imaging (T2WI) at day 7. Meanwhile DOX-LIP group and SPIO-DOX-LIP group showed that high-density lipiodol was deposited in the tumors in CT images. Histopathologic findings showed an almost complete central necrosis coagulation of tumors in the SPIO-DOX-LIP group, and the tumor necrosis percentage and tumor apoptosis index were significantly increased in the SPIO-DOX-LIP group compared to those in other 3 groups (P<0.05). Conclusion: This novel drug-delivery system of SPIO nano-drug carrier together with LIP is safe and feasible when it is used for transhepatic arterial embolization for liver tumor. It provides an excellent MR and CT visualization and improves the therapeutic efficacy for the treatment of rabbit VX2 liver tumor.

Optimization of the composition and dosage of PEGylated polyethylenimine-entrapped gold nanoparticles for blood pool, tumor, and lymph node CT imaging

Gold nanoparticles (Au NPs) with a high X-ray attenuation coefficient have a good potential in CT imaging applications. Here, we report the design and synthesis of Au NPs entrapped within polyethylene glycol (PEG)-modified branched polyethyleneimine (PEI) with varying the initial Au salt/PEI molar ratios and with the remaining PEI surface amines being acetylated for blood pool, lung tumor and lymph node CT imaging. The formed unacetylated and acetylated PEGylated PEI-entrapped Au NPs (Au PENPs) were characterized via different methods. We show that the PEGylated PEI is an effective template to entrap Au NPs having a uniform size ranging from 1.7nm to 4.4nm depending on the Au salt/PEI molar ratio. After optimization of the composition-dependent X-ray attenuation effect, we then selected {(Au0)100-PEI·NHAc-mPEG} NPs for biological testing and show that the particles have good cytocompatibility in the given concentration range and can be used as a contrast agent for effective CT imaging of the blood pool of rats, lung cancer model of nude mice and lymph node of rabbits after intravenous injection. For each application, the injected dosage of the particles was optimized. In addition, the {(Au0)100-PEI·NHAc-mPEG} NPs could be excreted out of the body with time. Our results indicate that the formed Au PENPs with an appropriate composition and dosage hold a great promise to be used for CT imaging of various biosystems.

Dendrimers meet zwitterions: development of a unique antifouling nanoplatform for enhanced blood pool, lymph node and tumor CT imaging.

We report the synthesis and characterization of antifouling zwitterion carboxybetaine acrylamide (CBAA)-modified dendrimer-entrapped gold nanoparticles (Au DENPs) for enhanced CT imaging applications. The CBAA-modified nanodevice displays a better protein resistance property, less macrophage cellular uptake and liver accumulation, and longer blood half-delay time than the PEGylated counterpart material, thereby enabling enhanced blood pool, lymph node, and tumor CT imaging.

Lung tumor segmentation algorithm

This paper is a development of an algorithm based medical image processing to segment the lung tumor in CT images due to the lack of such algorithms and approaches used to detect tumor where most of researches involve machine learning to solve such segmentation problem. The work involves different image processing tools which successfully achieved the required goals when combined and successively applied. The segmentation system comprises of different stages to finally reach its target which is to segment the lung tumor. Image pre-processing takes place first where some enhancement techniques are used to enhance and reduce noise in images. The next stage is where the different parts in the images are seperated to be able to segment the tumor in later stages. In this phase threshold was selected automatically which assures the right selection of all images since the tumor have different gray-levels intensities in each image. Another technique was also used here to remove the tumor from the thresholded image. Finally, the lung tumor is accurately segmented by subtracting the thresholded and the other image.

AuGd integrated nanoprobes for optical/MRI/CT triple-modal in vivo tumor imaging.

Multi-modal imaging agents are desirable for tumor diagnosis because they can provide more information on the tumor than single-modal imaging agents. However, most reported multi-modal imaging agents are dual-modal agents rather than tri-modal agents; therefore, detailed information on the tumor may still be insufficient when such imaging agents are used. To ameliorate this issue, we synthesized a tri-modal imaging agent, composed of gold cluster and gadolinium oxide integrated nanoparticles (denoted as AuGds) using bovine serum albumin (BSA) as the template via a bio-mineralization method. The AuGds exhibit red fluorescence at ∼660 nm for optical imaging, strong X-ray absorption (around 700 HU) for CT imaging, and a high r1 value (∼12.39 mM-1 s-1) for MR imaging. After being chemically modified with folic acid (FA), the AuGds can specifically target folate receptors on KB tumor cells, and permit in vivo optical, MR, and CT imaging of xenografted tumors. By comparing these three imaging modalities, very clear structural and anatomical information on the in vivo tumor can be obtained. The AuGds show good biocompatibility, quick renal clearance, and do not induce normal tissue toxicity in vivo.