COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training

Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e., only a subset of organs are annotated. Therefore, it is crucial to investigate how to learn a unified model on the available partially labeled datasets to leverage their synergistic potential. In this paper, we systematically investigate the partial-label segmentation problem with theoretical and empirical analyses on the prior techniques. We revisit the problem from a perspective of partial label supervision signals and identify two signals derived from ground truth and one from pseudo labels. We propose a novel two-stage framework termed COSST, which effectively and efficiently integrates comprehensive supervision signals with self-training. Concretely, we first train an initial unified model using two ground truth-based signals and then iteratively incorporate the pseudo label signal to the initial model using self-training. To mitigate performance degradation caused by unreliable pseudo labels, we assess the reliability of pseudo labels via outlier detection in latent space and exclude the most unreliable pseudo labels from each self-training iteration. Extensive experiments are conducted on one public and three private partial-label segmentation tasks over 12 CT datasets. Experimental results show that our proposed COSST achieves significant improvement over the baseline method, i.e., individual networks trained on each partially labeled dataset. Compared to the state-of-the-art partial-label segmentation methods, COSST demonstrates consistent superior performance on various segmentation tasks and with different training data sizes

Miniature ultrasound transducer incorporating Sm-PMN-PT 1-3 composite

Piezoelectric 1-3 composite materials have become extensively utilized in diagnostic ultrasound transducers owing to their high electromechanical coupling coefficient, low acoustic impedance, and low dielectric loss. In this study, Sm-doped PMN-PT ceramic/epoxy 1-3 composite with a ceramic volume fraction of 60% is fabricated by the dice-and-fill method, resulting in a high piezoelectric constant (650 pC/N) and clamped dielectric constant (2350). Utilizing the exceptionally high clamped dielectric constant, a low-frequency (12.4 MHz) ultrasound transducer is developed with a miniature aperture size (0.84 mm × 0.84 mm), exhibiting a −6 dB bandwidth of 70% and an insertion loss of −20.5 dB. The imaging capability of the miniature composite transducer is validated through both phantom and ex vivo imaging. The satisfactory results indicate that Sm-doped ceramic/epoxy composites possess significant potential for miniature devices in biomedical imaging applications

A miniature multi-functional photoacoustic probe

Photoacoustic technology is a promising tool to provide morphological and functional information in biomedical research. To enhance the imaging efficiency, the reported photoacoustic probes have been designed coaxially involving complicated optical/acoustic prisms to bypass the opaque piezoelectric layer of ultrasound transducers, but this has led to bulky probes and has hindered the applications in limited space. Though the emergence of transparent piezoelectric materials helps to save effort on the coaxial design, the reported transparent ultrasound transducers were still bulky. In this work, a miniature photoacoustic probe with an outer diameter of 4 mm was developed, in which an acoustic stack was made with a combination of transparent piezoelectric material and a gradient-index lens as a backing layer. The transparent ultrasound transducer exhibited a high center frequency of ~47 MHz and a −6 dB bandwidth of 29.4%, which could be easily assembled with a pigtailed ferrule of a single-mode fiber. The multi-functional capability of the probe was successfully validated through experiments of fluid flow sensing and photoacoustic imaging