US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration?

OBJECTIVES: Artificial intelligence (AI) systems can diagnose thyroid nodules with similar or better performance than radiologists. Little is known about how this performance compares with that achieved through fine needle aspiration (FNA). This study aims to compare the diagnostic yields of FNA cytopathology alone and combined with BRAFV600E mutation analysis and an AI diagnostic system. METHODS: The ultrasound images of 637 thyroid nodules were collected in three hospitals. The diagnostic efficacies of an AI diagnostic system, FNA-based cytopathology, and BRAFV600E mutation analysis were evaluated in terms of sensitivity, specificity, accuracy, and the κ coefficient with respect to the gold standard, defined by postsurgical pathology and consistent benign outcomes from two combined FNA and mutation analysis examinations performed with a half-year interval. RESULTS: The malignancy threshold for the AI system was selected according to the Youden index from a retrospective cohort of 346 nodules and then applied to a prospective cohort of 291 nodules. The combination of FNA cytopathology according to the Bethesda criteria and BRAFV600E mutation analysis showed no significant difference from the AI system in terms of accuracy for either cohort in our multicenter study. In addition, for 45 included indeterminate Bethesda category III and IV nodules, the accuracy, sensitivity, and specificity of the AI system were 84.44%, 95.45%, and 73.91%, respectively. CONCLUSIONS: The AI diagnostic system showed similar diagnostic performance to FNA cytopathology combined with BRAFV600E mutation analysis. Given its advantages in terms of operability, time efficiency, non-invasiveness, and the wide availability of ultrasonography, it provides a new alternative for thyroid nodule diagnosis. CLINICAL RELEVANCE STATEMENT: Thyroid ultrasonic artificial intelligence shows statistically equivalent performance for thyroid nodule diagnosis to FNA cytopathology combined with BRAFV600E mutation analysis. It can be widely applied in hospitals and clinics to assist radiologists in thyroid nodule screening and is expected to reduce the need for relatively invasive FNA biopsies. KEY POINTS: • In a retrospective cohort of 346 nodules, the evaluated artificial intelligence (AI) system did not significantly differ from fine needle aspiration (FNA) cytopathology alone and combined with gene mutation analysis in accuracy. • In a prospective multicenter cohort of 291 nodules, the accuracy of the AI diagnostic system was not significantly different from that of FNA cytopathology either alone or combined with gene mutation analysis. • For 45 indeterminate Bethesda category III and IV nodules, the AI system did not perform significantly differently from BRAFV600E mutation analysis.

Low blood flow ratio is associated with hemorrhagic transformation secondary to mechanical thrombectomy in patients with acute ischemic stroke.

BACKGROUND AND PURPOSE: A significant decrease of cerebral blood flow (CBF) is a risk factor for hemorrhagic transformation (HT) in acute ischemic stroke (AIS). This study aimed to ascertain whether the ratio of different CBF thresholds derived from computed tomography perfusion (CTP) is an independent risk factor for HT after mechanical thrombectomy (MT). METHODS: A retrospective single center cohort study was conducted on patients with AIS undergoing MT at the First Affiliated Hospital of Wenzhou Medical University from August 2018 to December 2023. The perfusion parameters before thrombectomy were obtained according to CTP automatic processing software. The low blood flow ratio (LFR) was defined as the ratio of brain volume with relative CBF <20 % over volume with relative CBF <30 %. HT was evaluated on the follow-up CT images. Binary logistic regression was used to analyze the correlation between parameters that differ between the two groups with regards to HT occurrence. The predictive efficacy was assessed utilizing the receiver operating characteristic curve. RESULTS: In total, 243 patients met the inclusion criteria. During the follow-up, 46.5 % of the patients (113/243) developed HT. Compared with the Non-HT group, the HT group had a higher LFR (0.47 (0.34-0.65) vs. 0.32 (0.07-0.56); P < 0.001). According to the binary logistic regression analysis, the LFR (aOR: 6.737; 95 % CI: 1.994-22.758; P = 0.002), Hypertension history (aOR: 2.231; 95 % CI: 1.201-4.142; P = 0.011), plasma FIB levels before MT (aOR: 0.641; 95 % CI: 0.456-0.902; P = 0.011), and the mismatch ratio (aOR: 0.990; 95 % CI: 0.980-0.999; P = 0.030) were independently associated with HT secondary to MT. The area under the curve of the regression model for predicting HT was 0.741. CONCLUSION: LFR, a ratio quantified via CTP, demonstrates potential as an independent risk factor of HT secondary to MT.

Application of advanced diffusion models from diffusion weighted imaging in a large cohort study of breast lesions.

BACKGROUND: To evaluate multiple parameters in multiple b-value diffusion-weighted imaging (DWI) in characterizing breast lesions and predicting prognostic factors and molecular subtypes. METHODS: In total, 504 patients who underwent 3-T magnetic resonance imaging (MRI) with T1-weighted dynamic contrast-enhanced (DCE) sequences, T2-weighted sequences and multiple b-value (7 values, from 0 to 3000 s/mm2) DWI were recruited. The average values of 13 parameters in 6 models were calculated and recorded. The pathological diagnosis of breast lesions was based on the latest World Health Organization (WHO) classification. RESULTS: Twelve parameters exhibited statistical significance in differentiating benign and malignant lesions. alpha demonstrated the highest sensitivity (89.5%), while sigma demonstrated the highest specificity (77.7%). The stretched-exponential model (SEM) demonstrated the highest sensitivity (90.8%), while the biexponential model demonstrated the highest specificity (80.8%). The highest AUC (0.882, 95% CI, 0.852-0.912) was achieved when all 13 parameters were combined. Prognostic factors were correlated with different parameters, but the correlation was relatively weak. Among the 6 parameters with significant differences among molecular subtypes of breast cancer, the Luminal A group and Luminal B (HER2 negative) group had relatively low values, and the HER2-enriched group and TNBC group had relatively high values. CONCLUSIONS: All 13 parameters, independent or combined, provide valuable information in distinguishing malignant from benign breast lesions. These new parameters have limited meaning for predicting prognostic factors and molecular subtypes of malignant breast tumors.

Radiomics analysis of ultrasound to predict recurrence of hepatocellular carcinoma after microwave ablation.

OBJECTIVE: To develop and validate an ultrasonic radiomics model for predicting the recurrence and differentiation of hepatocellular carcinoma (HCC). Convolutional neural network (CNN) ResNet 18 and Pyradiomics were used to analyze gray-scale-ultrasonic images to predict the prognosis and degree of differentiation of HCC. METHODS: This retrospective study enrolled 513 patients with HCC who underwent preoperative grayscale-ultrasonic imaging, and their clinical characteristics were observed. Patients were randomly divided into training (n = 413) and validation (n = 100) cohorts. CNN ResNet 18 and Pyradiomics were used to analyze ultrasonic images of HCC and peritumoral images to develop a prognostic and differentiation model. Clinical characteristics were integrated into the radiomics model and patients were stratified into high- and low-risk groups. The predictive effect was evaluated using the C-index and receiver operating characteristic (ROC) curve. RESULTS: The model combined with ResNet 18 and clinical characteristics achieved a good predictive ability. The C-indices of early recurrence (ER), late recurrence (LR), and recurrence-free survival (RFS) were 0.695 (0.561-0.789), 0.715 (0.623-0.800) and 0.721 (0.647-0.795), respectively, in the validation cohort, which was superior to the clinical model and ultrasonic semantic model. The model could stratify patients into high- and low-risk groups, which showed significant differences (p < 0.001) in ER, LR, and RFS. The area under the curve for predicting the degree of HCC differentiation was 0.855 and 0.709 in the training and validation cohorts, respectively. CONCLUSION: We developed and validated a radiomics model to predict HCC recurrence and HCC differentiation, which could also acquire pathological information in a noninvasive manner.KEY RESULTSA hepatocellular carcinoma (HCC) prognostic prediction model was developed and validated by convolutional neural network (CNN) ResNet 18-based gray-scale ultrasound (US).A differentiation of HCC prediction model was developed for preoperative prediction avoiding invasive operation.Compared with Pyradiomics, CNN ResNet was more suitable for extracting information from US images.

Techniques and Algorithms for Hepatic Vessel Skeletonization in Medical Images: A Survey.

Hepatic vessel skeletonization serves as an important means of hepatic vascular analysis and vessel segmentation. This paper presents a survey of techniques and algorithms for hepatic vessel skeletonization in medical images. We summarized the latest developments and classical approaches in this field. These methods are classified into five categories according to their methodological characteristics. The overview and brief assessment of each category are provided in the corresponding chapters, respectively. We provide a comprehensive summary among the cited publications, image modalities and datasets from various aspects, which hope to reveal the pros and cons of every method, summarize its achievements and discuss the challenges and future trends.

Histogram analysis of diffusion kurtosis imaging based on whole-volume images of breast lesions.

BACKGROUND: Breast diffusion kurtosis imaging (DKI) is a novel MRI technique to assess breast cancer but the effectivity still remains to be improved. PURPOSE: To investigate the performance of whole-volume histogram parameters derived from a DKI model for differentiating benign and malignant breast lesions. STUDY TYPE: Retrospective. POPULATION: In all, 120 patients with breast lesions (62 malignant, 58 benign). SEQUENCE: DKI sequence with seven b-values (0, 500, 1000, 1500, 2000, 2500, and 3000 s/mm2 ) and DWI sequence with two b-values (0 and 1000 s/mm2 ) on 3.0T MRI. ASSESSMENT: Histogram parameters of the DKI model (K and D) and the DWI model (ADC), including the minimum, maximum, mean, percentile values (25th, 50th, 75th, and 95th), standard deviation, kurtosis and skewness, were calculated by two radiologists for the whole lesion volume. STATISTICAL TESTS: Student's t-test was used to compare malignant and benign lesions. The diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis. RESULTS: Kmax , Dmin , and ADCmin had the highest area under the curve (AUC) (0.875, 0.830, and 0.847, respectively), sensitivity (85.5%, 74.2%, and 77.4%, respectively), and accuracy (85.0%, 79.2%, and 81.7%, respectively) in their individual histogram parameter groups, and Kmax was found to outperform Dmin and ADCmin . ADC histogram parameters (from ADCmin to ADCsd ) were significantly lower than D histogram parameters in all groups. DATA CONCLUSION: Kmax , Dmin , and ADCmin were found to be better metrics than the corresponding average values for differentiating benign from malignant tumors. Histogram parameters derived from the DKI model provided more information and had better diagnostic performance than ADC parameters derived from the DWI model. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:627-634.

Complications After Percutaneous Ultrasound-Guided Liver Biopsy: A Systematic Review and Meta-analysis of a Population of More Than 12,000 Patients From 51 Cohort Studies.

OBJECTIVES: Percutaneous liver biopsy (LB) has been considered the reference standard in distinguishing the degree of liver disease, but there has been no definitive systematic review to assess complication rates or potential risk factors for them. METHODS: In this study, we searched the PubMed, Embase, Web of Science, and Scopus databases for studies appraising complication rates after percutaneous ultrasound (US)-guided LB published until October 11, 2018. The safety and efficacy of US-guided LB were estimated according to major and minor complications. Subgroups including the biopsy style, needle styles, mean number of needle insertions, study period, and specific complication items were analyzed. RESULTS: Among 12,481 patients from 51 studies, pooled results showed a low rate (0; 95% confidence interval, 0-0) of major and minor complications. The subgroup analysis indicated that US-guided LB had a low major complication rate of 0 (0-0) for both fine-needle aspiration and core biopsy, with rates of 0.016 (0-0.032) for 14-gauge, 0.010 (0.003-0.017) for 15-gauge, 0.002 (-0.001-0.005) for 20-gauge, and 0 (0-0) for 16-, 17-, 18-, 21-, and 22-gauge needles, and low minor complication rates of 0 (0-0) for fine-needle aspiration and 0.001 (0-0.002) for core biopsy, with rates of 0.164 (0.137-0.191) for 15-gauge, 0.316 (0.113-0.519) for 16-gauge, and 0 (0-0) for 14-, 17-, 18-, 20-, 21-, and 22-gauge needles. Furthermore, specific complication rates of bleeding, pain, pneumothorax, vasovagal reactions, and death were all 0 (0-0). CONCLUSIONS: These findings suggest that it is possible to safely perform percutaneous US-guided LB.

Multifractal and lacunarity analyses of microvascular morphology in eyes with diabetic retinopathy: A projection artifact resolved optical coherence tomography angiography study.

OBJECTIVE: To evaluate the degree of microvascular impairment in DR using multifractal and lacunarity analyses and to compare the diagnostic ability between traditional Euclidean measures (fovea avascular zone area and vessel density) and fractal geometric features. METHODS: This retrospective cross-sectional study included a total of 143 eyes of 94 patients with different stages of DR. The retinal microvasculature was imaged by projection removed OCTA. We examined the degree of association between fractal metrics of the retinal microvasculature and DR severity. The area under the ROC curve was used to estimate the diagnostic performance. RESULTS: With increasing DR severity, the multifractal spectrum shifted toward the left bottom and exhibited less left skewness and asymmetry. The vessel density, multifractal features, and lacunarity measured from the DCP were strongly associated with DR severity. The multifractal feature D5 showed the highest diagnostic ability. The combination of multifractal features further improved the discriminating power. CONCLUSIONS: Multifractal and lacunarity analyses can be potentially valuable tools for assessment of microvascular impairments in DR. Multifractal geometric parameters exhibit a better discriminatory performance than Euclidean measures, particularly for detection of the early stages of DR.

Percutaneous Laser Ablation of Hepatic Tumors Located in the Portacaval Space: Preliminary Results.

BACKGROUND AND OBJECTIVES: Limited data have been used to evaluate the feasibility and effectiveness of percutaneous laser ablation (PLA) (a modality that has been proven to be a safe method for tumors in high-risk locations) for hepatic tumors in the space between the portal vein and inferior vena cava (IVC). The goal of this study was to investigate the characteristics and therapeutic effectiveness of ultrasound-guided PLA of hepatic tumors in the portacaval space. STUDY DESIGN/MATERIALS AND METHODS: Ten patients, who had hepatic tumors in the portacaval space (defined as tumors located in the space formed by the hepatic portal vein and IVC less than 5 mm from the margins of both vessels), receiving ultrasound-guided PLA between January 2016 and June 2017 were analyzed. Tumors in the type I portacaval space were enclosed on three sides of major vessels, and tumors in the type II portacaval space were enclosed on two sides of major vessels. The technical success, treatment response, complete tumor ablation (CTA), local tumor progression (LTP), and distant tumor recurrence (DTR) were assessed and recorded at the follow-up. RESULTS: The mean tumor diameter was 1.8 ± 0.4 cm. Technical success and initial CTA were achieved in all 10 patients without major complications. The 6-month and 12-month LTP rates were 0% and 10%, respectively. The DTR rate was 20% at both the 6- and 12-month follow-ups. CONCLUSIONS: The preliminary results showed that ultrasound-guided PLA was feasible and safe for tumors in the portacaval space, and further studies on larger populations with a longer follow-up are needed to delineate the use of PLA and evaluate its therapeutic efficacy. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.

Use of diffusion kurtosis imaging and quantitative dynamic contrast-enhanced MRI for the differentiation of breast tumors.

BACKGROUND: Breast MRI is a sensitive imaging technique to assess breast cancer but its effectiveness still remains to be improved. PURPOSE: To evaluate the diagnostic performance of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and quantitative dynamic contrast-enhanced (DCE)-MRI in differentiating malignant from benign breast lesions independently or jointly and to explore whether correlations exist among these parameters. STUDY TYPE: Retrospective. POPULATION: In all, 106 patients with breast lesions (47 malignant, 59 benign). SEQUENCE: DKI sequence with seven b values and quantitative DCE sequence on 3.0T MRI. ASSESSMENT: Diffusion parameters (mean diffusivity [MD], mean diffusivity [MK], and apparent diffusion coefficient [ADC]) from DKI and DWI and perfusion parameters from DCE (Ktrans , kep , ve , and vp ) were calculated by two experienced radiologists after postprocessing. Disagreement between the two observers was resolved by consensus. STATISTICAL TESTS: The parameters in benign and malignant lesions were compared by Student's t-test. The diagnostic performances of DKI and quantitative DCE, either alone or in combination, were evaluated by receiver operating characteristic (ROC) analysis. The Spearman correlation test was used to evaluate correlations among the diffusion parameters and perfusion parameters. RESULTS: MK, MD, ADC, Ktrans , and kep values were significantly different between breast cancer and benign lesions (P < 0.05). MK from DKI demonstrated the highest AUC of 0.849, which is significantly higher than ADC derived from conventional DWI (z = 3.345, P = 0.0008). The specificity of DCE-MRI-derived parameters was improved when combining diffusion parameters, such as ADC and MK. The highest diagnostic specificity (93.2%) was obtained when kep and ADC were combined. kep was correlated moderately positively with MK (r = 0.516) and moderately negatively with MD (r = -0.527). Ktrans was weakly positively correlated with MK with an r of 0.398 and weakly negatively correlated with MD with an r of -0.450. DATA CONCLUSION: DKI is more valuable than conventional DWI in distinguishing between benign and malignant breast lesions. DKI exhibits promise as a quantitative technique to augment quantitative DCE-MRI. Diffusion parameters derived from DKI were statistically correlated with perfusion parameters from quantitative DCE-MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1358-1366.

Densely connected convolutional networks for ultrasound image based lesion segmentation.

Delineating lesion boundaries play a central role in diagnosing thyroid and breast cancers, making related therapy plans and evaluating therapeutic effects. However, it is often time-consuming and error-prone with limited reproducibility to manually annotate low-quality ultrasound (US) images, given high speckle noises, heterogeneous appearances, ambiguous boundaries etc., especially for nodular lesions with huge intra-class variance. It is hence appreciative but challenging for accurate lesion segmentations from US images in clinical practices. In this study, we propose a new densely connected convolutional network (called MDenseNet) architecture to automatically segment nodular lesions from 2D US images, which is first pre-trained over ImageNet database (called PMDenseNet) and then retrained upon the given US image datasets. Moreover, we also designed a deep MDenseNet with pre-training strategy (PDMDenseNet) for segmentation of thyroid and breast nodules by adding a dense block to increase the depth of our MDenseNet. Extensive experiments demonstrate that the proposed MDenseNet-based method can accurately extract multiple nodular lesions, with even complex shapes, from input thyroid and breast US images. Moreover, additional experiments show that the introduced MDenseNet-based method also outperforms three state-of-the-art convolutional neural networks in terms of accuracy and reproducibility. Meanwhile, promising results in nodular lesion segmentation from thyroid and breast US images illustrate its great potential in many other clinical segmentation tasks.

MTANet: Multi-Task Attention Network for Automatic Medical Image Segmentation and Classification.

Medical image segmentation and classification are two of the most key steps in computer-aided clinical diagnosis. The region of interest were usually segmented in a proper manner to extract useful features for further disease classification. However, these methods are computationally complex and time-consuming. In this paper, we proposed a one-stage multi-task attention network (MTANet) which efficiently classifies objects in an image while generating a high-quality segmentation mask for each medical object. A reverse addition attention module was designed in the segmentation task to fusion areas in global map and boundary cues in high-resolution features, and an attention bottleneck module was used in the classification task for image feature and clinical feature fusion. We evaluated the performance of MTANet with CNN-based and transformer-based architectures across three imaging modalities for different tasks: CVC-ClinicDB dataset for polyp segmentation, ISIC-2018 dataset for skin lesion segmentation, and our private ultrasound dataset for liver tumor segmentation and classification. Our proposed model outperformed state-of-the-art models on all three datasets and was superior to all 25 radiologists for liver tumor diagnosis.

MRI non-rigid registration with tumor contraction correction for ablative margin assessment after microwave ablation of hepatocellular carcinomas.

Objective. This study aims to develop and assess a tumor contraction model, enhancing the precision of ablative margin (AM) evaluation after microwave ablation (MWA) treatment for hepatocellular carcinomas (HCCs).Approach. We utilize a probabilistic method called the coherent point drift algorithm to align pre-and post-ablation MRI images. Subsequently, a nonlinear regression method quantifies local tumor contraction induced by MWA, utilizing data from 47 HCC with viable ablated tumors in post-ablation MRI. After automatic non-rigid registration, correction for tumor contraction involves contracting the 3D contour of the warped tumor towards its center in all orientations.Main results. We evaluate the performance of our proposed method on 30 HCC patients who underwent MWA. The Dice similarity coefficient between the post-ablation liver and the warped pre-ablation livers is found to be 0.95 ± 0.01, with a mean corresponding distance between the corresponding landmarks measured at 3.25 ± 0.62 mm. Additionally, we conduct a comparative analysis of clinical outcomes assessed through MRI over a 3 month follow-up period, noting that the AM, as evaluated by our proposed method, accurately detects residual tumor after MWA.Significance. Our proposed method showcases a high level of accuracy in MRI liver registration and AM assessment following ablation treatment. It introduces a potentially approach for predicting incomplete ablations and gauging treatment success.

Time multiscale regularization for nonlinear image registration.

Regularization-based methods are commonly used for image registration. However, fixed regularizers have limitations in capturing details and describing the dynamic registration process. To address this issue, we propose a time multiscale registration framework for nonlinear image registration in this paper. Our approach replaces the fixed regularizer with a monotone decreasing sequence, and iteratively uses the residual of the previous step as the input for registration. Particularly, first, we introduce a dynamically varying regularization strategy that updates regularizers at each iteration and incorporates them with a multiscale framework. This approach guarantees an overall smooth deformation field in the initial stage of registration and fine-tunes local details as the images become more similar. We then deduce convergence analysis under certain conditions on the regularizers and parameters. Further, we introduce a TV-like regularizer to demonstrate the efficiency of our method. Finally, we compare our proposed multiscale algorithm with some existing methods on both synthetic images and pulmonary computed tomography (CT) images. The experimental results validate that our proposed algorithm outperforms the compared methods, especially in preserving details during image registration with sharp structures.

MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion.

Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects.

UDRSNet: An unsupervised deformable registration module based on image structure similarity.

BACKGROUND: Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field. PURPOSE: To propose a simple and reliable method for registering medical images based on image structure similarity in a completely unsupervised manner. METHODS: We proposed a deep cascade unsupervised deformable registration approach to align images without reliable clinical data labels. Our basic network was composed of a displacement estimation module (ResUnet) and a deformation module (spatial transformer layers). We adopted l2 -norm to regularize the deformation field instead of the traditional l1 -norm regularization. Additionally, we utilized structural similarity (ssim) estimation during the training stage to enhance the structural consistency between the deformed images and the reference images. RESULTS: Experiments results indicated that by incorporating ssim loss, our cascaded methods not only achieved higher dice score of 0.9873, ssim score of 0.9559, normalized cross-correlation (NCC) score of 0.9950, and lower relative sum of squared difference (SSD) error of 0.0313 on CT images, but also outperformed the comparative methods on ultrasound dataset. The statistical t-test results also proved that these improvements of our method have statistical significance. CONCLUSIONS: In this study, the promising results based on diverse evaluation metrics have demonstrated that our model is simple and effective in deformable image registration (DIR). The generalization ability of the model was also verified through experiments on liver CT images and cardiac ultrasound images.

MR-guided percutaneous microwave coagulation of small breast tumors.

BACKGROUND: To evaluate the technical success and patient safety of magnetic resonance-guided percutaneous microwave coagulation (MR-guided PMC) for breast malignancies. METHODS: From May 2018 to December 2019, 26 patients with breast tumors measuring 2 cm or less were recruited to participate in a prospective, single-institution clinical study. The primary endpoint of this study was the evaluation of treatment efficacy for each patient. Histochemical staining with α-nicotinamide adenine dinucleotide and reduced (NADH)-diaphorase was used to determine cell viability following and efficacy of PMC. The complications and self-reported sensations from all patients during and after ablation were also assessed. The technical success of the PMC procedure was defined when the area of the NADH-diaphorase negative region fully covered the hematoxylin-eosin (H&E) staining region in the tumor. RESULTS: All patients had a complete response to ablation with no residual carcinoma on histopathological specimen. The mean energy, ablation duration, and procedure duration per tumor were 36.0 ± 4.2 kJ, 252.9 ± 30.9 S, and 104.2 ± 13.5 min, respectively. During the ablation, 14 patients underwent prolonged ablation time, and 1 patient required adjusting of the antenna position. Eleven patients had feelings of subtle heat or swelling, and 3 patients experienced slight pain. After ablation, one patient took two painkillers because of moderate pain, and no patients had postoperative oozing or other complications after PMC. Induration around the ablation area appeared in 16 patients. CONCLUSION: MR-guided PMC of small breast tumors is feasible and could be applied in clinical practice in the future. CRITICAL RELEVANCE STATEMENT: MR-guided PMC of small breast tumors is feasible and could be applied in clinical practice in the future. KEY POINTS: • MR-guided PMC of small breast tumors is feasible. • PMC was successfully performed for all patients. • All patients were satisfied with the final cosmetic result.

EPolar-UNet: An edge-attending polar UNet for automatic medical image segmentation with small datasets.

BACKGROUND: Medical image segmentation is one of the most key steps in computer-aided clinical diagnosis, geometric characterization, measurement, image registration, and so forth. Convolutional neural networks especially UNet and its variants have been successfully used in many medical image segmentation tasks. However, the results are limited by the deficiency in extracting high resolution edge information because of the design of the skip connections in UNet and the need for large available datasets. PURPOSE: In this paper, we proposed an edge-attending polar UNet (EPolar-UNet), which was trained on the polar coordinate system instead of classic Cartesian coordinate system with an edge-attending construction in skip connection path. METHODS: EPolar-UNet extracted the location information from an eight-stacked hourglass network as the pole for polar transformation and extracted the boundary cues from an edge-attending UNet, which consisted of a deconvolution layer and a subtraction operation. RESULTS: We evaluated the performance of EPolar-UNet across three imaging modalities for different segmentation tasks: CVC-ClinicDB dataset for polyp, ISIC-2018 dataset for skin lesion, and our private ultrasound dataset for liver tumor segmentation. Our proposed model outperformed state-of-the-art models on all three datasets and needed only 30%-60% of training data compared with the benchmark UNet model to achieve similar performances for medical image segmentation tasks. CONCLUSIONS: We proposed an end-to-end EPolar-UNet for automatic medical image segmentation and showed good performance on small datasets, which was critical in the field of medical image segmentation.

Asymmetric-backed multi-frequency ultrasonic transducer for conformal tumor ablation.

OBJECTIVE: Minimally invasive ultrasound ablation transducers have been widely studied. However, conventional designs are limited by the single working frequency, restricting their conformal ablation ability (i.e. ablation size and shape controllability). METHODS: New multi-frequency ultrasonic transducer design method is proposed based on the asymmetric backing layer, which divides the transducer into non-backing-layer region (i.e. front-piezoelectric region) and backing-layer region (i.e. front-piezoelectric-backing region) with multiple local thickness mode resonant frequencies. Ablation zone can be controlled by exciting the local resonance within or between the regions, and its control flexibility is further enhanced by driven under a multi-frequency modulation signal. Experiments and calculations are combined for verifying the proposal. RESULTS: The fabricated transducer with a Y-direction asymmetric backing layer shows five resonances, with two in each region and one resonance excited in both regions. Spatial ultrasound emission is demonstrated by acoustic measurements. Tissue ablation experiments verified spatial ablation zone control, and frequency modulation driving method enables the spatial transition of ablation zone from one region to the other, generating different ablation sizes and shapes. Finally, patient-specific simulations verified the effectiveness of conformal ablation. CONCLUSION: The proposed transducer enables flexible control of ablation zone. SIGNIFICANCE: This study demonstrates a new method for conformal tumor ablation.