Si-MSPDNet: A multiscale Siamese network with parallel partial decoders for the 3-D measurement of spines in 3D ultrasonic images

Early screening and frequent monitoring effectively decrease the risk of severe scoliosis, but radiation exposure is a consequence of traditional radiograph examinations. Additionally, traditional X-ray images on the coronal or sagittal plane have difficulty providing three-dimensional (3-D) information on spinal deformities. The Scolioscan system provides an innovative 3-D spine imaging approach via ultrasonic scanning, and its feasibility has been demonstrated in numerous studies. In this paper, to further examine the potential of spinal ultrasonic data for describing 3-D spinal deformities, we propose a novel deep-learning tracker named Si-MSPDNet for extracting widely employed landmarks (spinous process (SP)) in ultrasonic images of spines and establish a 3-D spinal profile to measure 3-D spinal deformities. Si-MSPDNet has a Siamese architecture. First, we employ two efficient two-stage encoders to extract features from the uncropped ultrasonic image and the patch centered on the SP cut from the image. Then, a fusion block is designed to strengthen the communication between encoded features and further refine them from channel and spatial perspectives. The SP is a very small target in ultrasonic images, so its representation is weak in the highest-level feature maps. To overcome this, we ignore the highest-level feature maps and introduce parallel partial decoders to localize the SP. The correlation evaluation in the traditional Siamese network is also expanded to multiple scales to enhance cooperation. Furthermore, we propose a binary guided mask based on vertebral anatomical prior knowledge, which can further improve the performance of our tracker by highlighting the potential region with SP. The binary-guided mask is also utilized for fully automatic initialization in tracking. We collected spinal ultrasonic data and corresponding radiographs on the coronal and sagittal planes from 150 patients to evaluate the tracking precision of Si-MSPDNet and the performance of the generated 3-D spinal profile. Experimental results revealed that our tracker achieved a tracking success rate of 100% and a mean IoU of 0.882, outperforming some commonly used tracking and real-time detection models. Furthermore, a high correlation existed on both the coronal and sagittal planes between our projected spinal curve and that extracted from the spinal annotation in X-ray images. The correlation between the tracking results of the SP and their ground truths on other projected planes was also satisfactory. More importantly, the difference in mean curvatures was slight on all projected planes between tracking results and ground truths. Thus, this study effectively demonstrates the promising potential of our 3-D spinal profile extraction method for the 3-D measurement of spinal deformities using 3-D ultrasound data.

Three-Dimensional Assessment of Automated Breast Volume Scanner Compared with Handheld Ultrasound in Pre-Operative Breast Invasive Ductal Carcinomas: A Pilot Study of 51 Cases

The aim of the work described here was to compare the accuracy of conventional handheld ultrasound (HHUS) with that of an automated breast volume scanner (ABVS) in 3-D assessment of pre-operative invasive ductal carcinomas. HHUS and ABVS were used in 51 patients to obtain the largest tumor diameter, tumor volume and tumor surface area. The volumetric measurement was also obtained from ABVS data with medical software. With tumor size and volume on pathology as the gold standard, Bland-Altman analysis was used to compare variability. Correlation coefficients and receiver operating characteristic curves were established for all measurements for T2 classification. The correlation coefficients of all ABVS measurements were stronger than those of HHUS measurements, with the ABVS volumetric measurement significantly different with a higher accuracy of 88.24% (45/51) and predicting T-classification with higher area under the receiver operating characteristic curves (0.936). Therefore, 3-D measurements provide stronger correlations with pathology in tumor size measurement. However, more clinical trials are needed to confirm our findings.