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Objective: To present the comprehensive methodology for generating personalized three-dimensional (3D) printed uterine models from 3D ultrasound (US) volumes in individuals diagnosed with Müllerian anomalies and discuss potential applications in the field of reproductive endocrinology and infertility. Design: Pilot study. Setting: Single large university-affiliated teaching hospital. Patient(s): Patients with the presence of a Müllerian anomaly between the ages of 18 and 45 years attending the maternal-fetal medicine as well as reproductive endocrinology and infertility outpatient offices from 2018 to 2023 were included in the study. Intervention(s): Subjects underwent 3D US transvaginal scanning for the collection of data. The 3D US volumes were acquired, edited, and exported from a US cart Voluson E10 system (GE Healthcare, Chicago, IL). High-definition virtual models were created and modified, making them suitable for printing using Materialise 3-Matic Medical (Materialise NV, Leuven, Belgium). The models were printed on a J5 MediJet 3D printer (Stratasys, Rehovot, Israel). Colors were set to mimic a realistic appearance, and shore values were set before printing. Main Outcome Measure(s): Successful creation and utilization of personalized 3D-printed uterine models for individuals with Müllerian anomalies. Results(s): Three-dimensional models were created for a uterus without anomalies, 2 variations of a partial septum, a unicornuate, and a didelphys uterus. Models were used as a tactile and customized tool for patient education, counseling, and medical student and resident teaching. This technique illustrates that the creation of personalized 3D-printed uterine models for utilization in the fields of reproductive endocrinology and infertility is feasible. Conclusion(s): We propose a novel use of individualized 3D-printed uterine models in the evaluation of individuals with Müllerian anomalies. These models may play a complementary role to standard imaging options in the assessment of these anomalies, with a special potential for application in highly complex or yet-to-be-determined types of anomalies.

With the success of U-Net or its variants in automatic medical image segmentation, building a fully convolutional network (FCN) based on an encoder-decoder structure has become an effective end-to-end learning approach. However, the intrinsic property of FCNs is that as the encoder deepens, higher-level features are learned, and the receptive field size of the network increases, which results in unsatisfactory performance for detecting low-level small/thin structures such as atrial walls and small arteries. To address this issue, we propose to keep the different encoding layer features at their original sizes to constrain the receptive field from increasing as the network goes deeper. Accordingly, we develop a novel S-shaped multiple cross-aggregation segmentation architecture named S-Net, which has two branches in the encoding stage, i.e., a resampling branch to capture low-level fine-grained details and thin/small structures and a downsampling branch to learn high-level discriminative knowledge. In particular, these two branches learn complementary features by residual cross-aggregation; the fusion of the complementary features from different decoding layers can be effectively accomplished through lateral connections. Meanwhile, we perform supervised prediction at all decoding layers to incorporate coarse-level features with high semantic meaning and fine-level features with high localization capability to detect multi-scale structures, especially for small/thin volumes fully. To validate the effectiveness of our S-Net, we conducted extensive experiments on the segmentation of cardiac wall and intracranial aneurysm (IA) vasculature, and quantitative and qualitative evaluations demonstrated the superior performance of our method for predicting small/thin structures in medical images.

Percutaneous interventions are gaining rapid acceptance in cardiology and revolutionizing the treatment of structural heart disease (SHD). As new percutaneous procedures of SHD are being developed, their associated complexity and anatomical variability demand a high-resolution special understanding for intraprocedural image guidance. During the last decade, three-dimensional (3D) transesophageal echocardiography (TEE) has become one of the most accessed imaging methods for structural interventions. Although 3D-TEE can assess cardiac structures and functions in real-time, its limitations (e.g., limited field of view, image quality at a large depth, etc.) must be addressed for its universal adaptation, as well as to improve the quality of its imaging and interventions. This review aims to present the role of TEE in the intraprocedural guidance of percutaneous structural interventions. We also focus on the current and future developments required in a multimodal image integration process when using TEE to enhance the management of congenital and SHD treatments.