Objective To evaluate the accuracy of contrast-enhanced harmonic EUS （CEH-EUS） by using a second-generation contrast medium in differential diagnosis of pancreatic occupying lesions. Methods Patients with suspected pancreatic neoplasms or chronic pancreatitis were enrolled and underwent CEH-EUS by using ultrasonic contrast medium Sonovue. Cytological and/or histological diagnoses were made by EUS-FNA and the final follow-up results. Characteristics of enhancement of the target areas, such as enhancement sequence, time features, pattern grade and venous elution degree were investigated. Results A total of 23 patients were enrolled, in which 13 were diagnosed with FNA as having pancreatic cancer, 7 with chronic pancreatitis, 2 with intraductal papillary mucinous neoplasms and 1 with microcystic serous cystadenoma. The accuracy of CEH-EUS was 95.65% , which was significantly higher than that of conventional EUS （78.2%）. Enhancement of pancreatic cancer by CEH-EUS was later than or simultaneous with the nearby tissues, with heterogeneous low enhancement or fulfillment defect areas, and early subsidence without obvious peak. Enhancement of benign pancreatic diseases was simultaneous with the surrounding tissues, with homogenous fulfillment and simultaneous subsidence. Conclusion CEH-EUS is safe, convenient and accurate in diagnosis of pancreatic occupying lesions and can be used as an additional diagnostic method to EUSFNA.
Key words:
Pancreatic diseases ; Endoscopic uhrasonography ; Diagnosis ; Contrast enhanced harmonic
Objective To obtain the distribution of regions with high probability of cancer.Methods The method of Multi-scaled partition and the original classification of pancreatic endoscopic ultrasonography(Eus)images was combined.The original method of classification was improved according to the new feature of partition,and 23 texture features were extracted.Results Totally 216 EUS images were used as training and testing samples and various random experiments were taken.Thus,the probability of cancer in each block was obtained and displaged.Conclusion This method can provide a reference position for implanting radioactive particles.
Aims: Eus-guided radioactive seeds implantion has been found to be effective that may improve quality of life and survival in patients with advanced unresectable pancreatic cancer. The evaluation of it's effects remains a challenging clinical problem.
The diagnosis of risk level of gastrointestinal stromal tumor (GIST) is of great clinical significance. The morphology of GIST in endoscopic ultrasound (EUS) images has been normally used by radiologists to diagnosis the risk level of GISTs. Hence, accurate segmentation of GISTs in EUS images is a crucial factor to influence the diagnosis. U-net, an elegant network, has been commonly used in medical images. However, due to the plain architecture and complicated up-sampling path of U-net, classical U-net does not perform well in segmenting GISTs in EUS images with diverse size, heavy shadow and ambiguous boundary. Hence, this paper proposes a novel multi-task refined boundary-supervision U-net (MRBSU-net) for GIST segmentation in EUS images. In our network, multi-task refined U-net (RU-net) is set to deal with heavy shadow and diverse size. Boundary cross entropy in loss function of multi-task RU-net boosts the influence of small size tumors and the refinement avoid the noise information in EUS images propagating to the higher resolution layers. Then we design a refined boundary-supervision U-net (RBSU-net) to solve the ambiguous problem. The boundary supervision in RBSU-net leads the network focus on finding boundary in the down-sampling part and segmenting region on the up-sampling path. At last, we put multi-task RU-net in front of the RBSU-net to increase the stability of the network, what is called MRBSU-net. Extensive experiments have been designed to evaluate the performance of the proposed network. The comparison experiments include the results from traditional U-net, generative adversarial network (GAN) and Deep Attentional Features (DAF). The results of our proposed method perform best among all the comparison methods, which proves that the proposed network could be potentially used in clinic.
Objective To develop and evaluate the digital discrimination system for pancreatic ultrasound endoscopy images. Methods EUS images of 153 pancreatic cancer and 63 non-cancer cases were selected. According to the multi-fractal feature vectors based on the M-band wavelet transform, we acquired the fractal features with lower dimension with the feature screening algorithm. With the optimal feature com- bination, cases were classified into pancreatic cancer group and non-pancreatic cancer group automatically. Then the sensitivity, specificity and accuracy of this method were calculated, and compared with those of tra- ditional 9 dimension fractal feature vectors. Results Three kinds of muhi-fractal dimensions were intro- duced to the framework of M-band wavelet transform according to the EUS images to form fractal vectors of 18 dimension. With the selection by sequence forward search （SFS） algorithm, 7 dimension of feature vectors were chosen and were combined with bi-order muhi-fractal dimension to a better feature combination. The Bayes, support vector machine （SVM） and ModestAdaBoost classifiers were introduced to evaluate the clas- sification efficiency, resulting in a classification accuracy of 97.98% and short running time of 0. 49 s with lower feature dimension. Conclusion These data suggest the feasibility, accuracy, noninvasiveness and efficacy of classification of EUS images to differentiate pancreatic cancer from normal tissue based on the Mband wavelet transform algorithm. It is a new and valuable research area in diagnosis of pancreatic cancer.
Key words:
Pancreatic cancer; Endoscopic ultrasonography; M-band wavelet transform; Multifractal
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