Self-training with dual uncertainty for semi-supervised medical image segmentation

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimatio

A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification

The segmentation and classification of carotid plaques in ultrasound images play important roles in the treatment of atherosclerosis and assessment for the risk of stroke. Although deep learning methods have been used for carotid plaque segmentation and classification, two-stage methods will increase the complexity of the overall analysis and the existing multi-task methods ignored the relationship between the segmentation and classification. These will lead to suboptimal performance as valuable information might not be fully leveraged across all tasks. Therefore, we propose a multi-task learning framework (RCCM-Net) for ultrasound carotid plaque segmentation and classification, which utilizes a region confidence module (RCM) and a sample category confidence module (CCM) to exploit the correlation between these two tasks. The RCM provides knowledge from the probability of plaque regions to the classification task, while the CCM is designed to learn the categorical sample weight for the segmentation task. A total of 1270 2D ultrasound images of carotid plaques were collected from Zhongnan Hospital (Wuhan, China) for our experiments. The results showed that the proposed method can improve both segmentation and classification performance compared to existing single-task networks (i.e., SegNet, Deeplabv3+, UNet++, EfficientNet, Res2Net, RepVGG, DPN) and multi-task algorithms (i.e., HRNet, MTANet), with an accuracy of 85.82% for classification and a Dice-similarity-coefficient of 84.92% for segmentation. In the ablation study, the results demonstrated that both the designed RCM and CCM were beneficial in improving the network's performance. Therefore, we believe that the proposed method could be useful for carotid plaque analysis in clinical trials and practice

Dual uncertainty-guided multi-model pseudo-label learning for semi-supervised medical image segmentation

Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However, this method relies on the quality of pseudo-labels and can lead to an unstable training process due to differences between samples. Additionally, directly generating pseudo-labels from the model itself accelerates noise accumulation, resulting in low-confidence pseudo-labels. To address these issues, we proposed a dual uncertainty-guided multi-model pseudo-label learning framework (DUMM) for semi-supervised medical image segmentation. The framework consisted of two main parts: The first part is a sample selection module based on sample-level uncertainty (SUS), intended to achieve a more stable and smooth training process. The second part is a multi-model pseudo-label generation module based on pixel-level uncertainty (PUM), intended to obtain high-quality pseudo-labels. We conducted a series of experiments on two public medical datasets, ACDC2017 and ISIC2018. Compared to the baseline, we improved the Dice scores by 6.5% and 4.0% over the two datasets, respectively. Furthermore, our results showed a clear advantage over the comparative methods. This validates the feasibility and applicability of our approach

Čimbenik prirodne obnove vrste Platycladus orientalis (L.) Franco u Guilinu, Kina

Cypress (Platycladus orientalis (L.) Franco) is one of the important evergreen trees for afforestation in barren mountains, soil consolidation, and water conservation, but natural regeneration of cypress is complex and slow. An understanding of the influence mechanism of the natural regeneration of cypresses is essential for elevating survival and regeneration. This study aimed to clarify the relationship between stand factors, environmental factors, and regeneration of cypress plantations. A total of 42 cypress sample plots in Guilin, China, were selected to evaluate the impact of various stand factors and environmental factors on the regeneration of cypresses using survey statistics and Pearson and Spearmanrank correlation analysis. In this study, cypress has the highest frequency and density of regeneration among all the seedlings in the 18 surveyed forests, but the height structure of cypress seedlings distributes in uneven mode and mainly Grade I (height < 30 cm) seedlings. Low-density herbs and high-density moss mulching had a directly positive effect on the number of cypress regeneration seedlings. Larger soil stone content and gap area can promote cypress regeneration, which is appropriate for cypresses in the seedling stage. In conclusion, timely weeding, proper soil loosening, and improving light transmittance contribute to promoting the regeneration of cypresses.Obična azijska tuja (Platycladus orientalis (L.) Franco) je jedno od važnih zimzelenih stabala za pošumljavanje u neplodnim planinama, za konsolidaciju tla i očuvanje vode, ali prirodna regeneracija obične azijske tuje je složena i spora. Razumijevanje mehanizma utjecaja na prirodnu regeneraciju obične azijske tuje bitno je za poticanje preživljavanja i regeneraciju. Cilj ovog istraživanja je objasniti odnos između šumskih sastojina, okolišnih čimbenika i regeneracije nasada obične azijske tuje. Ukupno 42 uzorka čempresa u Guilinu, Kina, odabrane su za procjenu utjecaja različitih čimbenika sastojine i okolišnih čimbenika na regeneraciju čempresa korištenjem statistike ankete i korelacijske analize Pearson i Spearmanrank. U ovoj studiji, obična azijska tuja ima najveću učestalost i gustoću regeneracije među svim sadnicama ( 18 ) u istraživanoj šumi, ali je visinska struktura sadnica obične azijske tuje raspoređena neravnomjerno, a radi se uglavnom o sadnicama I klase (visina < 30 cm). Bilje niske gustoće i malčiranje mahovine velike gustoće, imali su izravan pozitivan učinak na broj sadnica za regeneraciju obične azijske tuje. Veći sadržaj kamena u tlu i opustošenih površina mogu potaknuti regeneraciju obične azijske tuje, ali je to prikladnije za obične azijske tuje u fazi sadnice. Zaključno, pravodobno plijevljenje korova, pravilno rahljenje tla i poboljšanje propusnosti svjetla mogli bi potaknuti regeneraciju obične azijske tuje

Performance analysis of RIS-assisted cell-free massive MIMO systems with transceiver hardware impairments

Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated