A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets

Detecting the organ boundary in an ultrasound image is challenging because of the poor contrast of ultrasound images and the existence of imaging artifacts. In this study, we developed a coarse-to-refinement architecture for multi-organ ultrasound segmentation. First, we integrated the principal curve-based projection stage into an improved neutrosophic mean shift-based algorithm to acquire the data sequence, for which we utilized a limited amount of prior seed point information as the approximate initialization. Second, a distribution-based evolution technique was designed to aid in the identification of a suitable learning network. Then, utilizing the data sequence as the input of the learning network, we achieved the optimal learning network after learning network training. Finally, a scaled exponential linear unit-based interpretable mathematical model of the organ boundary was expressed via the parameters of a fraction-based learning network. The experimental outcomes indicated that our algorithm 1) achieved more satisfactory segmentation outcomes than state-of-the-art algorithms, with a Dice score coefficient value of 96.68 ± 2.2%, a Jaccard index value of 95.65 ± 2.16%, and an accuracy of 96.54 ± 1.82% and 2) discovered missing or blurry areas.

ACU2E-Net: A novel predict-refine attention network for segmentation of soft-tissue structures in ultrasound images

Segmentation of anatomical structures in ultrasound images is a challenging task due to existence of artifacts inherit to the modality such as speckle noise, attenuation, shadowing, uneven textures and blurred boundaries. This paper presents a novel attention-based predict-refine network, called ACU2E-Net, for segmentation of soft-tissue structures in ultrasound images. The network consists of two modules: a predict module, which is built upon our newly proposed attentive coordinate convolution; and a novel multi-head residual refinement module, which consists of three parallel residual refinement modules. The attentive coordinate convolution is designed to improve the segmentation accuracy by perceiving the shape and positional information of the target anatomy. The proposed multi-head residual refinement module reduces both segmentation biases and variances by integrating residual refinement and ensemble strategies. Moreover, it avoids multi-pass training and inference commonly seen in ensemble methods. To show the effectiveness of our method, we collect a comprehensive dataset of thyroid ultrasound scans from 12 different imaging centers, and evaluate our proposed network against state-of-the-art segmentation methods. Comparisons against state-of-the-art models demonstrate the competitive performance of our newly designed network on both the transverse and sagittal thyroid images. Ablation studies show that proposed modules improve the segmentation Dice score of the baseline model from 79.62% to 80.97% and 82.92% while reducing the variance from 6.12% to 4.67% and 3.21% in transverse and sagittal views, respectively.

Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentation

Liver Ultrasound (US) or sonography is popularly used because of its real-time output, low-cost, ease-of-use, portability, and non-invasive nature. Segmentation of real-time liver US is essential for diagnosing and analyzing liver conditions (e.g., hepatocellular carcinoma (HCC)), assisting the surgeons/radiologists in therapeutic procedures. In this paper, we propose a method using a modified Pyramid Scene Parsing (PSP) module in tuned neural network backbones to achieve real-time segmentation without compromising the segmentation accuracy. Considering widespread noise in US data and its impact on outcomes, we study the impact of pre-processing and the influence of loss functions on segmentation performance. We have tested our method after annotating a publicly available US dataset containing 2400 images of 8 healthy volunteers (link to the annotated dataset is provided); the results show that the Dense-PSP-UNet model achieves a high Dice coefficient of 0.913±0.024 while delivering a real-time performance of 37 frames per second (FPS).

ASU-Net++: A nested U-Net with adaptive feature extractions for liver tumor segmentation

Locating tumors from medical images is of high importance in medical analysis and diagnosis. To tackle the complicated shape of tumors, we propose a multi-leves.l feature extraction neural network to automatically segment the data. Our proposed model is trained and tested with one liver tumor ultrasound and two CT datasets. We employ ++, a collaborative model that uses modified nested U-Net, as our backbone. The model is integrated with dilated dense short skip connections within convolution blocks to further improve the gradient flow and feature preservation. In addition, we modify the original Atrous Spatial Pyramid Pooling (ASPP) to an adaptive pooling structure for better compatibility with nested U-Net. Adaptive ASPP is designed to extract features from different levels and cover the increasing range of feature extraction with regard to the depth of the nested network. Our model showed its advantage in accurately segmenting different tumor sizes with complex edges and was able to generalize with small and diverse datasets. We further improved our model with the newly introduced AdaBelief optimizer and achieved a faster convergence rate. Segmentation results showed that the proposed model outperformed multiple network structures, and achieved a 0.9153 dice coefficient for the ultrasound dataset, a 0.9413 and a 0.9246 dice coefficient for the two CT datasets.