Segmentation and visualization of nasopharyngeal carcinoma using MRI

Efficacy evaluation of different measurement methods for target lesions after neoadjuvant chemotherapy and radical radiotherapy in locally advanced hypopharyngeal carcinoma

AIM

To assess the diagnostic efficacy in response evaluation of hypopharyngeal carcinoma (HPC) using different CT measurement methods.

METHODS AND MATERIALS

One hundred and three patients with locally advanced HPC receiving neoadjuvant chemotherapy (NACT) and radical radiotherapy (RT) were retrospectively enrolled. The long diameter, short diameter and largest axial area of the tumors and the largest metastatic cervical lymph node (LN) were measured before and after NACT, at the end of RT and 1 month after RT. Tumor regression ratios of the sum of the tumor's long diameter and LN's short diameter (LDTSDL), the sum of tumor and LN's short diameter (TTSDL), the sum of tumor and LN's largest axial area (AATML) were calculated. Analysis was conducted for overall survival (OS), metastasis-free survival, regional recurrence-free survival (RRFS), and local recurrence-free survival (LRFS).

RESULTS

Note that 35, 28, 23, and 16 patients suffered death, local recurrence, regional recurrence and distant metastasis, respectively. TTSDL-defined effective group demonstrated better LRFS (p = .039) and RRFS (p = .047) after NACT and better OS since the end of RT (p = .037); AATML-defined effective groups demonstrated better OS, LRFS, and RRFS since the end of RT (p = .015, .008, and .005). While LDTSDL-defined groups showed differences in OS and LRFS until 1 month after RT (p = .013 and .014).

CONCLUSIONS

The regression rate of TTSDL and AATML can distinguish prognosis at an earlier time and demonstrated better reliability compared with LDTSDL. They were recommended for response evaluation in HPC.

Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study

Purpose

In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images.

Materials and Methods

PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm).

Results

A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P < 0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively.

Conclusion

A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.

Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network

Objectives

To evaluate the application of a deep learning architecture, based on the convolutional neural network (CNN) technique, to perform automatic tumor segmentation of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma (NPC).

Materials and Methods

In this prospective study, 87 MRI containing tumor regions were acquired from newly diagnosed NPC patients. These 87 MRI were augmented to >60,000 images. The proposed CNN network is composed of two phases: feature representation and scores map reconstruction. We designed a stepwise scheme to train our CNN network. To evaluate the performance of our method, we used case-by-case leave-one-out cross-validation (LOOCV). The ground truth of tumor contouring was acquired by the consensus of two experienced radiologists.

Results

The mean values of dice similarity coefficient, percent match, and their corresponding ratio with our method were 0.89±0.05, 0.90±0.04, and 0.84±0.06, respectively, all of which were better than reported values in the similar studies.

Conclusions

We successfully established a segmentation method for NPC based on deep learning in contrast-enhanced magnetic resonance imaging. Further clinical trials with dedicated algorithms are warranted.

Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in medical images is an import task for clinical applications, including radiotherapy. However, NPC features large variations in lesion size and shape, as well as inhomogeneous intensities within the tumor and similar intensity to that of nearby tissues, making its segmentation a challenging task. The present study proposes a novel automated NPC segmentation method in magnetic resonance (MR) images by combining a deep convolutional neural network (CNN) model and a 3-dimensional (3D) graph cut-based method in a two-stage manner. First, a multi-view deep CNN-based segmentation method is performed. A voxel-wise initial segmentation is generated by integrating the inferential classification information of three trained single-view CNNs. Instead of directly using the CNN classification results to achieve a final segmentation, the proposed method uses a 3D graph cut-based method to refine the initial segmentation. Specifically, the probability response map obtained using the multi-view CNN method is utilized to calculate the region cost, which represents the likelihood of a voxel being assigned to the tumor or non-tumor. Structure information in 3D from the original MR images is used to calculate the boundary cost, which measures the difference between the two voxels in the 3D neighborhood. The proposed method was evaluated on T1-weighted images from 30 NPC patients using the leave-one-out method. The experimental results demonstrated that the proposed method is effective and accurate for NPC segmentation.

Unidimensional measurement may be superior to assess primary tumor response after neoadjuvant chemotherapy for nasopharyngeal carcinoma

Application of current response evaluation criteria in solid tumors (RECIST 1.1) for assessment of irregularly shaped nasopharyngeal carcinoma (NPC) is a gray area with much ambiguity. Our aim was to compare unidimensional measurements (UDM) and bidimensional measurements (BDM) on magnetic resonance images in alternative planes for measurement of tumor response after neoadjuvant chemotherapy (NACT) in patients with locally advanced NPC. 59 patients with untreated non-metastatic NPC were prospectively enrolled. The size or change in size of the primary tumor and retropharyngeal nodes was assessed by UDM and BDM on axial and coronal planes before and after 2 cycles of NACT. Tumor volume was considered as the reference standard. Correlation between volume and diameter was analyzed using a general linear model. The degree of agreement and discordance of response classification based on different measures were evaluated with κ statistic and McNemar's test, respectively. Both axial UDM (RECIST 1.1) and axial BDM (WHO) showed a significant association with volumetric standard. However, the agreement of axial UDM with VM was better than that of axial BDM (κ value: 0.514 to 0.372). In addition, when increasing coronal planes to evaluate tumor response with UDM and BDM, an inferior agreement between coronal BDM and VM was still observed. Notably, coronal UDM showed the best consistency with volume (κ = 0.585). Hence, axial UDM showed better correlation with volumetric measurements than axial BDM. Since coronal UDM showed high correlation to VM, we suggest further research to assess its use for response assessment of NPC after NACT.

Nasopharyngeal carcinoma segmentation via HMRF-EM with maximum entropy

This paper presents a novel automatic nasopharyngeal carcinoma segmentation approach used in magnetic resonance images. Adaptive calculation of the nasopharyngeal region location is first performed. The contour of the tumor is determined through distance regularized level set evolution with the initial contour obtained by the nearest neighbor graph model. To further refine the segmentation, a hidden Markov random field model with maximum entropy (HMRF-EM) is introduced to model the spatial information with prior knowledge. The proposed method is tested on magnetic resonance images of 26 nasopharyngeal carcinoma patients, and achieves good results.

Measuring steady-state cerebral vasomotor reactivity using non-triggered phase-contrast magnetic resonance imaging

To investigate cVMR by using CO2-based hypercapnic challenge and blood flow monitoring employing non-triggered phase contrast (PC) magnetic resonance imaging. Six healthy volunteers (6 male; mean age: 29 years) participated this study after providing institutionally approved consent. This study used non-triggered PC imaging to increase temporal resolution of dynamic blood flow measurements, allowing real-time monitoring of the hypercapnic challenge response. Results suggest that vasomotor reactivity measured by non-triggered PC imaging is positively associated with the concentration of inhaled CO2. This study concludes that CO2 challenge combined with non-triggered PC flow imaging is potentially useful to provide diagnostic information for patients with cerebrovascular disease.

Region-based nasopharyngeal carcinoma lesion segmentation from MRI using clustering- and classification-based methods with learning

In clinical diagnosis of nasopharyngeal carcinoma (NPC) lesion, clinicians are often required to delineate boundaries of NPC on a number of tumor-bearing magnetic resonance images, which is a tedious and time-consuming procedure highly depending on expertise and experience of clinicians. Computer-aided tumor segmentation methods (either contour-based or region-based) are necessary to alleviate clinicians' workload. For contour-based methods, a minimal user interaction to draw an initial contour inside or outside the tumor lesion for further curve evolution to match the tumor boundary is preferred, but parameters within most of these methods require manual adjustment, which is technically burdensome for clinicians without specific knowledge. Therefore, segmentation methods with a minimal user interaction as well as automatic parameters adjustment are often favored in clinical practice. In this paper, two region-based methods with parameters learning are introduced for NPC segmentation. Two hundred fifty-three MRI slices containing NPC lesion are utilized for evaluating the performance of the two methods, as well as being compared with other similar region-based tumor segmentation methods. Experimental results demonstrate the superiority of adopting learning in the two introduced methods. Also, they achieve comparable segmentation performance from a statistical point of view.

Quantitative characterization and identification of lymph nodes and nasopharingeal carcinoma by coregistered magnetic resonance images

In this study we developed a technique to improve the identification of carcinoma and pathological lymph nodes in cases of Nasopharingeal Carcinoma (NPC), through a quantitative characterization of the tissues based on MR images: 3D VIBE (Volumetric Interpolated Breath-hold Examination) T1-CE (Contrast Enhanced), T1, T2 and Diffusion Weighted Imaging (DWI) for b-values 0,300,500,700,1000. The procedure included two phases: 1) coregistration of volumes and 2) tissue characterization. Concerning the first phase, the DICOM images were reassembled spatially and resampled with isotropic 0.5mm resolution. Coregistration was performed by two multiresolution rigid transformations, merging head and neck volumes, plus a final multiresolution non rigid transformation. The anatomical 3D CE-VIBE volume was taken as reference. The procedure for tissue characterization is semi automated and it required a radiologist to identify an example of tissue from the primary tumor and a metastatic lymph node. We generated a 8-dimensional membership function to perform a fuzzy-like identification of these tissues. The result of this procedure was the generation of two maps, which showed complementary characterization of lymph nodes and carcinoma. A few example will be shown to evidence the potentiality of this method in identification and characterization of NPC lesions.

Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

Background

In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.

This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain.

Methods

The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system.

Results

The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.

Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related.

Conclusions

Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.

Semi-automated segmentation of the sigmoid and descending colon for radiotherapy planning using the fast marching method

A fast and accurate segmentation of organs at risk, such as the healthy colon, would be of benefit for planning of radiotherapy, in particular in an adaptive scenario. For the treatment of pelvic tumours, a great challenge is the segmentation of the most adjacent and sensitive parts of the gastrointestinal tract, the sigmoid and descending colon. We propose a semi-automated method to segment these bowel parts using the fast marching (FM) method. Standard 3D computed tomography (CT) image data obtained from routine radiotherapy planning were used. Our pre-processing steps distinguish the intestine, muscles and air from connective tissue. The core part of our method separates the sigmoid and descending colon from the muscles and other segments of the intestine. This is done by utilizing the ability of the FM method to compute a specified minimal energy functional integrated along a path, and thereby extracting the colon centre line between user-defined control points in the sigmoid and descending colon. Further, we reconstruct the tube-shaped geometry of the sigmoid and descending colon by fitting ellipsoids to points on the path and by adding adjacent voxels that are likely voxels belonging to these bowel parts. Our results were compared to manually outlined sigmoid and descending colon, and evaluated using the Dice coefficient (DC). Tests on 11 patients gave an average DC of 0.83 (+/-0.07) with little user interaction. We conclude that the proposed method makes it possible to fast and accurately segment the sigmoid and descending colon from routine CT image data.

Extraction of brain tumor from MR images using one-class support vector machine

A novel image segmentation approach by exploring one-class support vector machine (SVM) has been developed for the extraction of brain tumor from magnetic resonance (MR) images. Based on one-class SVM, the proposed method has the ability of learning the nonlinear distribution of the image data without prior knowledge, via the automatic procedure of SVM parameters training and an implicit learning kernel. After the learning process, the segmentation task is performed. The proposed technique is applied to 24 clinical MR images of brain tumor for both visual and quantitative evaluations. Experimental results suggest that the proposed query-based approach provides an effective and promising method for brain tumor extraction from MR images with high accuracy.

Radiotherapy-related typing in 842 patients in canton with nasopharyngeal carcinoma

PURPOSE

The aim of this study was to propose the clinical radiotherapy-related typing and to summarize proportional distribution of radiotherapy-related types of nasopharyngeal carcinoma (NPC).

METHODS AND MATERIALS

A total of 842 cases of NPC were randomly selected. According to 5-year follow-up results after radiotherapy, NPC was subdivided into four types: Type I (no primary and regional recurrence and no distant metastasis), Type II (primary or regional recurrence and no distant metastasis), Type III (no primary and regional recurrence, and distant metastasis), and Type IV (primary or regional recurrence, and distant metastasis). Proportion of the four types and relationship between this typing and Zhi-guang Xie typing were analyzed.

RESULTS

Distribution of radiotherapy-related types of NPC were 50.6%, 23.2%, 20.7%, and 5.6% for Types I, II, III, and IV, respectively. For Types D and AD of Zhi-guang Xie typing system and Stage III and IV of the 1992 Fuzhou staging system, the proportion of Type III was greater than that of Type II; and for Type A and Stage I and II, there was a larger proportion of Type II than that of Type III.

CONCLUSION

Radiotherapy-related typing, as a new clinical subclassification, could be supplementary for previous clinical typing and staging.

The relationship between nasopharyngeal carcinoma tumor volume and TNM T-classification: a quantitative analysis

Recent findings show that tumor volume is a significant prognostic factor for the treatment of nasopharyngeal carcinoma (NPC). The inclusion of tumor volume as an additional prognostic factor in the UICC TNM classification system was suggested; however, how tumor volume could possibly be incorporated is still unexplored. In this paper, we report a quantitative analysis on the relationship between NPC tumor volume and T-classification, using the data from 206 NPC patients. By T-classification and semi-automatic tumor volume measurement, the difference in tumor volumes among the various TNM T-classification groups was examined. In addition, a statistics-based analysis scheme, which used the T-classification as the "gold standard", was proposed to classify NPC tumors into volume-based groups to explore the possible links. The results show that NPC tumor volume has positive correlation with advancing T-classification groups and significant difference existed in the distribution of T-classification among various volume-based groups (P < 0.001). By the proposed statistical scheme, tumor volume could be included as an additional prognostic factor in the TNM framework, following validation studies.

Correlation between MR imaging–derived nasopharyngeal carcinoma tumor volume and TNM system

PURPOSE

To measure nasopharyngeal carcinoma tumor volume based on magnetic resonance images using a validated semiautomated measurement methodology and correlate tumor volume with TNM T classification.

METHODS AND MATERIALS

The study population consisted of 206 consecutive nasopharyngeal carcinoma patients who had magnetic resonance imaging staging scans. Tumor volume was measured using a semisupervised knowledge-based fuzzy clustering algorithm. Patients were divided into 4 groups according to TNM T classification. The difference in tumor volumes among the various TNM T-classification groups was examined.

RESULTS

The mean tumor volume in each T-classification group is as follows: T1, 8.6 mL +/- 5.0 (standard deviation [SD]); T2, 18.1 mL +/- 8.1 (SD); T3, 25.8 mL +/- 14.1 (SD); and T4, 36.2 mL +/- 18.9 (SD). The mean tumor volume increased significantly with advancing T classification (p < 0.0001). Tumor volume in a more advanced T group was significantly larger than that in an adjacent early T group (p < 0.01).

CONCLUSION

Validated magnetic resonance imaging-based tumor volume shows positive correlation between tumor volume and advancing T-classification groups. It may be possible to incorporate tumor volume as an additional prognostic factor into the existing TNM system.

Segmentation of nasopharyngeal carcinoma (NPC) lesions in MR images

PURPOSE

An accurate and reproducible method to delineate tumor margins from uninvolved tissues is of vital importance in guiding radiation therapy (RT). In nasopharyngeal carcinoma (NPC), tumor margin may be difficult to identify in magnetic resonance (MR) images, making the task of optimizing RT treatment more difficult. Our aim in this study is to develop a semiautomatic image segmentation method for NPC that requires minimal human intervention and is capable of delineating tumor margins with good accuracy and reproducibility.

METHODS AND MATERIALS

The segmentation algorithm includes 5 stages: masking, Bayesian probability calculation, smoothing, thresholding and seed growing, and finally dilation and overlaying of results with different thresholds. The algorithm is based on information obtained from the contrast enhancement ratio of T1-weighted images and signal intensity of T2-weighted images. The algorithm is initiated by the selection of a valid anatomical seed point within the tumor by the user. The algorithm was evaluated on MR images from 7 NPC patients and was compared against the radiologist's reference outline.

RESULTS

The algorithm was successfully implemented on all 7 subjects. With a threshold of 1, the average percent match is 78.5 +/- 3.86 (standard deviation) %, and the correspondence ratio is 66.5 +/- 7%.

DISCUSSION

The segmentation algorithm presented here may be useful for diagnosing NPC and may guide RT treatment planning. Further improvement will be desirable to improve the accuracy and versatility of the method.