Preliminary Clinical Experience with a Novel Optical-Ultrasound Imaging Device on Various Skin Lesions.

A compact handheld skin ultrasound imaging device has been developed that uses co-registered optical and ultrasound imaging to provide diagnostic information about the full skin depth. The aim of the current work is to present the preliminary clinical results of this device. Using additional photographic, dermoscopic and ultrasonic images as reference, the images from the device were assessed in terms of the detectability of the main skin layer boundaries and characteristic image features. Combined optical-ultrasonic recordings of various types of skin lesions (melanoma, basal cell carcinoma, seborrheic keratosis, dermatofibroma, naevus, dermatitis and psoriasis) were taken with the device (N = 53) and compared with images captured with a reference portable skin ultrasound imager. The investigator and two additional independent experts performed the evaluation. The detectability of skin structures was over 90% for the epidermis, the dermis and the lesions. The morphological and echogenicity information observed for the different skin lesions were found consistent with those of the reference ultrasound device and relevant ultrasound images in the literature. The presented device was able to obtain simultaneous in-vivo optical and ultrasound images of various skin lesions. This has the potential for further investigations, including the preoperative planning of skin cancer treatment.

Automated Skin Lesion Classification on Ultrasound Images.

The growing incidence of skin cancer makes computer-aided diagnosis tools for this group of diseases increasingly important. The use of ultrasound has the potential to complement information from optical dermoscopy. The current work presents a fully automatic classification framework utilizing fully-automated (FA) segmentation and compares it with classification using two semi-automated (SA) segmentation methods. Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained on 62 features, with ten-fold cross-validation. Six classification tasks were considered, namely all the possible permutations of one class versus one or two remaining classes. The receiver operating characteristic (ROC) area under the curve (AUC) as well as the accuracy (ACC) were measured. The best classification was obtained for the classification of nevi from cancerous lesions (melanoma, basal cell carcinoma), with AUCs of over 90% and ACCs of over 85% obtained with all segmentation methods. Previous works have either not implemented FA ultrasound-based skin cancer classification (making diagnosis more lengthy and operator-dependent), or are unclear in their classification results. Furthermore, the current work is the first to assess the effect of implementing FA instead of SA classification, with FA classification never degrading performance (in terms of AUC or ACC) by more than 5%.

Automated seeding for ultrasound skin lesion segmentation.

The segmentation of cancer-suspicious skin lesions using ultrasound may help their differential diagnosis and treatment planning. Active contour models (ACM) require an initial seed, which when manually chosen may cause variations in segmentation accuracy. Fully-automated skin segmentation typically employs layer-by-layer segmentation using a combination of methods; however, such segmentation has not yet been applied on cancerous lesions. In the current work, fully automated segmentation is achieved in two steps: an automated seeding (AS) step using a layer-by-layer method followed by a growing step using an ACM. The method was tested on images of nevi, melanomas, and basal cell carcinomas from two ultrasound imaging systems (N=60), with all lesions being successfully located. For the seeding step, manual seeding (MS) was used as a reference. AS approached the accuracy of MS when the latter used an optimal bounding rectangle based on the ground truth (Sørensen-Dice coefficient (SDC) of 72.3 vs 74.6, respectively). The effect of varying the manual seed was also investigated; a 0.7 decrease in seed height and width caused a mean SDC of 54.6. The results show the robustness of automated seeding for skin lesion segmentation.

A real-time data-based scan conversion method for single element ultrasound transducers.

The current work investigates the performance of a real-time scan conversion algorithm for generating a 2-D ultrasound image from a laterally scanned single-element ultrasound transducer, which has applications in point-of-care devices such as for skin imaging. The algorithm employs a fixed calibration curve to update a predefined image grid in real time. Simulations showed that the calibration curve (with a maximum of 1) is robust to changes in scatterer concentration (8.3×10-3 mean absolute error), signal to noise ratio (1.0×10-3 mean absolute error for -5 dB SNR), and can be accurately predicted from a small number (31) of point scatterers (6.9×10-3 mean absolute error). Good agreement was also found between the calibration curves obtained from simulated and experimental data (1.19×10-2 mean absolute error). The scan conversion algorithm was validated by evaluation of the position estimation errors on both simulations and experiments. Clinical images of skin lesions (N = 20) demonstrate the feasibility of the algorithm for real, non-homogeneous tissue. Use of a fixed calibration curve compared to an adaptive calibration curve gave similar accuracies in the scanning step size range of 150-350 µm (with an average overlap of the accuracy ranges of 92.94% for simulations and 42.83% for experiments), and a 350-fold improvement in computation time.