Breast Tumor Classification using Short-ResNet with Pixel-based Tumor Probability Map in Ultrasound Images.

Breast cancer is the most common form of cancer and is still the second leading cause of death for women in the world. Early detection and treatment of breast cancer can reduce mortality rates. Breast ultrasound is always used to detect and diagnose breast cancer. The accurate breast segmentation and diagnosis as benign or malignant is still a challenging task in the ultrasound image. In this paper, we proposed a classification model as short-ResNet with DC-UNet to solve the segmentation and diagnosis challenge to find the tumor and classify benign or malignant with breast ultrasonic images. The proposed model has a dice coefficient of 83% for segmentation and achieves an accuracy of 90% for classification with breast tumors. In the experiment, we have compared with segmentation task and classification result in different datasets to prove that the proposed model is more general and demonstrates better results. The deep learning model using short-ResNet to classify tumor whether benign or malignant, that combine DC-UNet of segmentation task to assist in improving the classification results.

Influence of temperature on sonographic images of the median nerve for the diagnosis of carpal tunnel syndrome: a case control study.

BACKGROUND: In addition to nerve conduction studies (NCSs), ultrasonography has been widely used as an alternative tool for diagnosing carpal tunnel syndrome (CTS). Although the results of NCSs are influenced by local skin temperature, few studies have explored the effects of skin temperature on ultrasonography of the median nerve. Since swelling and intraneural blood flow of the median nerve might be influenced by local temperature changes, the aim of this study was to evaluate the cross-sectional area (CSA) and intraneural blood flow of the median nerve under three skin temperatures (30 °C, 32 °C, 34 °C). METHODS: Fifty patients with CTS and 50 healthy volunteers were consecutively recruited from a community hospital. Each participant received physical examinations and NCSs and underwent ultrasonography, including power Doppler, to evaluate intraneural vascularity. RESULTS: The CSA of the median nerve in the CTS patients was significantly larger than that in the healthy controls at all three temperatures. However, significant differences in the power Doppler signals of the median nerve between the two studied groups were observed only at 30 and 32 °C, not at 34 °C. CONCLUSION: The significant difference in the intraneural vascularity of the median nerve between the patients with CTS and the healthy subjects was lost at higher temperatures (34 °C). Therefore, the results of power Doppler ultrasonography in diagnosing CTS should be cautiously interpreted in patients with a high skin temperature or those who reside in warm environments.

Effects of wrist extension on median nerve and flexor tendon excursions in patients with carpal tunnel syndrome: a case control study.

BACKGROUND: Reduced gliding ability of the median nerve in the carpal tunnel has been observed in patients with carpal tunnel syndrome (CTS). The purpose of this study was to evaluate the gliding abilities of the median nerve and flexor tendon in patients with CTS and healthy participants in the neutral and 30° extended positions of the wrist and to compare the gliding between the finger flexion and extension phases. METHODS: Patients with CTS and healthy participants were consecutively recruited in a community hospital. All the subjects received the Boston CTS questionnaire, physical examinations, nerve conduction study (NCS), and ultrasonography of the upper extremities. Duplex Doppler ultrasonography was performed to evaluate the gliding abilities of the median nerve and flexor tendon when the subjects continuously moved their index finger in the neutral and 30° extension positions of the wrist. RESULTS: Forty-nine patients with CTS and 48 healthy volunteers were consecutively recruited. Significant differences in the Boston CTS questionnaire, physical examination and NCS results and the cross-sectional area of the median nerve were found between the patients and the healthy controls. The degree of median nerve gliding and the ratio of median nerve excursion to flexor tendon excursion in the CTS group were significantly lower than those in the healthy control group in both the neutral and 30° wrist extension positions. Significantly increased excursion of both the median nerve and flexor tendon from the neutral to the extended positions were found in the CTS group. The ratio of median nerve excursion to flexor tendon excursion was significantly higher in the finger flexion phase than in the extended phase in both groups, and this ratio had mild to moderate correlations with answers on the Boston CTS Questionnaire and with the NCS results. CONCLUSIONS: Reduced excursion of the median nerve was found in the patients with CTS. The ratio of median nerve excursion to flexor tendon excursion was significantly lower in the patients with CTS than in the healthy volunteers. The median nerve excursion was increased while the wrist joint was extended to 30° in the patients with CTS. Wrist extension may be applied as part of the gliding exercise regimen for patients with CTS to improve median nerve mobilization.

Ultrasonographical Evaluation of the Median Nerve Mobility in Carpal Tunnel Syndrome: A Systematic Review and Meta-Analysis.

Diagnostic ultrasound is widely used for evaluating carpal tunnel syndrome (CTS), an entrapment neuropathy of the median nerve (MN). Decreased mobility of the MN inside the carpal tunnel has been reported in CTS, and various methods have been used to evaluate MN mobility; however, there is still no conclusive understanding of its connection with CTS. The purpose of this study is to conduct a systematic review and meta-analysis of the current published literature on ultrasonographic evaluations of transverse and longitudinal MN displacement and to identify the relationship between MN mobility and CTS. This study was conducted in accordance with the 2020 PRISMA statement and the Cochrane Collaboration Handbook. Comparative studies that investigated differences in MN displacement between CTS patients and healthy controls were retrieved by searching the Cochrane Library, Embase and PubMed. A total of 15 case-control studies were included. Nine of 12 studies evaluating transverse MN displacement and 4 of 5 studies evaluating longitudinal MN gliding showed that the MN was less mobile in CTS patients than in healthy subjects. Despite the large heterogeneity among the 15 included studies, this systematic review and meta-analysis provide evidence that the mobility of the MN is significantly reduced in both transverse and longitudinal planes in CTS patients compared to healthy controls. Five of the 15 included studies reported that a decrease in transverse or longitudinal MN displacement in CTS was correlated with clinical symptoms or with severity as measured by a nerve conduction study (NCS).

Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning.

Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nmet prediction is still a challenge for the patient in managing the surgical plan and making treatment decisions. We proposed a multi-energy level fusion model with a principal feature enhancement (PFE) block incorporating radiologist and computer science knowledge for Nmet prediction. The proposed model is custom-designed by gemstone spectral imaging (GSI) with different energy levels on dual-energy computer tomography (CT) from a primary tumor of lung cancer. In the experiment, we take three different energy level fusion datasets: lower energy level fusion (40, 50, 60, 70 keV), higher energy level fusion (110, 120, 130, 140 keV), and average energy level fusion (40, 70, 100, 140 keV). The proposed model is trained by lower energy level fusion that is 93% accurate and the value of Kappa is 86%. When we used the lower energy level images to train the fusion model, there has been a significant difference to other energy level fusion models. Hence, we apply 5-fold cross-validation, which is used to validate the performance result of the multi-keV model with different fusion datasets of energy level images in the pathology report. The cross-validation result also demonstrates that the model with the lower energy level dataset is more robust and suitable in predicting the Nmet of the primary tumor. The lower energy level shows more information of tumor angiogenesis or heterogeneity provided the proposed fusion model with a PFE block and channel attention blocks to predict Nmet from primary tumors.

Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning.

Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the preoperative prediction of nodal metastasis remains a challenge to determine surgical plans and pretreatment decisions in patients with cancers. We proposed a novel deep prediction method with a size-related damper block for nodal metastasis (Nmet) identification from the primary tumor in lung cancer generated by gemstone spectral imaging (GSI) dual-energy computer tomography (CT). The best model is the proposed method trained by the 40 keV dataset achieves an accuracy of 86 % and a Kappa value of 72 % for Nmet prediction. In the experiment, we have 11 different monochromatic images from 40∼140 keV (the interval is 10 keV) for each patient. When we used the model of 40 keV dataset, there has significant difference in other energy levels (unit of keV). Therefore, we apply in 5-fold cross-validation to explain the lower keV is more efficient to predict Nmet of the primary tumor. The result shows that tumor heterogeneity and size contributed to the proposed model to estimate whether absence or presence of nodal metastasis from the primary tumor.

MNT-DeepSL: Median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions.

Carpal tunnel syndrome (CTS) is a clinical disease that caused by the compression of median nerve within carpal tunnel. Traditional examining for CTS is electrodiagnostic (EDx), but the evaluation of EDx is more expensive and time-consuming. In the present day, ultrasound (US) image is used to clinical examining to make up the lack of nerve electrical inspection. The diagnostic criteria of US image for CTS are also defined in many researches. In this study, we propose a new tracking model with deep similarity learning for median nerve from CTS US images. Six wrist motions are defined in the clinical rehabilitation, and the proposed method can achieve accuracy more than 90 % for median nerve tracking. In the experiment, we discover some wrist motions, such as hook to full fist, the statistical information of median nerve tracking is more significant (P < 0.001). It means that some wrist motions are more easily to diagnose the problem of median nerve, and can be used as a basis for quick examining for CTS.

Tamper detection and recovery for medical images using near-lossless information hiding technique.

Digital medical images are very easy to be modified for illegal purposes. For example, microcalcification in mammography is an important diagnostic clue, and it can be wiped off intentionally for insurance purposes or added intentionally into a normal mammography. In this paper, we proposed two methods to tamper detection and recovery for a medical image. A 1024 x 1024 x-ray mammogram was chosen to test the ability of tamper detection and recovery. At first, a medical image is divided into several blocks. For each block, an adaptive robust digital watermarking method combined with the modulo operation is used to hide both the authentication message and the recovery information. In the first method, each block is embedded with the authentication message and the recovery information of other blocks. Because the recovered block is too small and excessively compressed, the concept of region of interest (ROI) is introduced into the second method. If there are no tampered blocks, the original image can be obtained with only the stego image. When the ROI, such as microcalcification in mammography, is tampered with, an approximate image will be obtained from other blocks. From the experimental results, the proposed near-lossless method is proven to effectively detect a tampered medical image and recover the original ROI image. In this study, an adaptive robust digital watermarking method combined with the operation of modulo 256 was chosen to achieve information hiding and image authentication. With the proposal method, any random changes on the stego image will be detected in high probability.

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration.

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.

Classification of breast ultrasound images using fractal feature.

Fractal analyses have been applied successfully for the image compression, texture analysis, and texture image segmentation. The fractal dimension could be used to quantify the texture information. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Furthermore, a computer-aided diagnosis (CAD) system based on the fractal analysis is proposed to classify the breast lesions into two classes: benign and malignant. To improve the classification performances, the ultrasound images are preprocessed by using morphology operations and histogram equalization. Finally, the k-means classification method is used to classify benign tumors from malignant ones. The US breast image databases include only histologically confirmed cases: 110 malignant and 140 benign tumors, which were recorded. All the digital images were obtained prior to biopsy using by an ATL HDI 3000 system. The receiver operator characteristic (ROC) area index AZ is 0.9218, which represents the diagnostic performance.

Three-dimensional ultrasound in margin evaluation for breast tumor excision using Mammotome.

Sonographic evidence of tumor removal by Mammotome excision does not confirm histological clearance. The operator finds it hard to determine if a malignant tumor has been fully removed, leaving a safe margin in the direction of each border; that is, the spatial orientation during tumor retrieval is not well-established by naked eye under sonographic guidance. We propose a computational imaging process to extract reasonable tumor contour in pre- and postoperative data sets for sonographic guidance so that Mammotome excision can help the operator to evaluate the surgical outcome. There were five tumors in the study, including three benign and two malignant. The lesion of interest was delineated after 2-D examination was completed, then it was analyzed with 3-D breast ultrasound (US). To give a reference point for correlations between pre- and postoperative images, we used a marker tape pasted on the skin within the transducer scanning area and then the preoperative 3-D US images were obtained. Subsequently, 2-D breast US was applied during Mammotome operation. After the Mammotome procedures were finished, the postoperative 3-D US images were obtained; thus, we gained two different data sets of 3-D US images that were used for later analysis for evaluating the extension of postoperative margin status. From the results, the safe margin was not satisfactory in all directions, because the minimum differences measured by the proposed algorithm were not large enough in all five cases, and this was proved from two malignant mastectomy specimens. The experimental results representing this inadequate Mammotome excision can be visualized through the computer aid. The comparison of tumor contour and excision margin may possibly be used for small malignant tumors in the future to improve the breast-conserving surgery.

Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression.

PURPOSE: To prospectively evaluate the accuracy of continuous ultrasonographic (US) images obtained during probe compression and computer-aided analysis for classification of biopsy-proved (reference standard) benign and malignant breast tumors. MATERIALS AND METHODS: This study was approved by the local ethics committee, and informed consent was obtained from all included patients. Serial US images of 100 solid breast masses (60 benign and 40 malignant tumors) were obtained with US probe compression in 86 patients (mean age, 45 years; range, 20-67 years). After segmentation of tumor contours with the level-set method, three features of strain on tissue from probe compression--contour difference, shift distance, area difference--and one feature of shape--solidity-were computed. A maximum margin classifier was used to classify the tumors by using these four features. The Student t test and receiver operating characteristic curve analysis were used for statistical analysis. RESULTS: The mean values of contour difference, shift distance, area difference, and solidity were 3.52% +/- 2.12 (standard deviation), 2.62 +/- 1.31, 1.08% +/- 0.85, and 1.70 +/- 1.85 in malignant tumors and 9.72% +/- 4.54, 5.04 +/- 2.79, 3.17% +/- 2.86, and 0.53 +/- 0.63 in benign tumors, respectively. Differences with P < .001 were statistically significant for all four features. Area under the receiver operating characteristic curve (A(Z)) values for contour difference, shift distance, area difference, and solidity were 0.88, 0.85, 0.86, and 0.79, respectively. The A(Z) value of three features of strain was significantly higher than that of the feature of shape (P < .01). The accuracy, sensitivity, specificity, and positive and negative predictive values of US classifications that were based on values for these four features were 87.0% (87 of 100), 85% (34 of 40), 88% (53 of 60), 83% (34 of 41), and 90% (53 of 59), respectively, with an A(Z) value of 0.91. CONCLUSION: Continuous US images obtained with probe compression and computer-aided analysis can aid in classification of benign and malignant breast tumors.