C2FResMorph:A high-performance framework for unsupervised 2D medical image registration

Deformable medical image registration is an important precursor task for surgical automation, while enhancing the registration performance of 2D medical images remains a challenging work. Existing methods primarily minimize the similarity loss between image pairs as the main optimization objective, leading to limited registration accuracy and a lack of pixel matching. Moreover, the scarcity of informative features in 2D images often results in overfitting on the training set, hampering generalization. To address these issues, we propose C2FResMorph, a learning-based deformable registration algorithm specifically designed for 2D medical images. C2FResMorph employs a two-stage framework that improves registration accuracy and preserves topology during deformation in a coarse-to-fine manner. Inside the framework, by leveraging the convolutional neural network's locality and the multi-head self-attention mechanism's globality, a ResMorph registration network is designed. Additionally, the integration of residual image knowledge addresses deformation folding in 2D image registration, enhancing the preservation of local structures and improving generalization. Experimental evaluations on three datasets demonstrate that C2FResMorph outperforms existing learning-based methods in terms of accuracy, generalization ability for 2D medical image registration, and also retains the efficiency advantages.</p

C2FResMorph:A high-performance framework for unsupervised 2D medical image registration

Deformable medical image registration is an important precursor task for surgical automation, while enhancing the registration performance of 2D medical images remains a challenging work. Existing methods primarily minimize the similarity loss between image pairs as the main optimization objective, leading to limited registration accuracy and a lack of pixel matching. Moreover, the scarcity of informative features in 2D images often results in overfitting on the training set, hampering generalization. To address these issues, we propose C2FResMorph, a learning-based deformable registration algorithm specifically designed for 2D medical images. C2FResMorph employs a two-stage framework that improves registration accuracy and preserves topology during deformation in a coarse-to-fine manner. Inside the framework, by leveraging the convolutional neural network's locality and the multi-head self-attention mechanism's globality, a ResMorph registration network is designed. Additionally, the integration of residual image knowledge addresses deformation folding in 2D image registration, enhancing the preservation of local structures and improving generalization. Experimental evaluations on three datasets demonstrate that C2FResMorph outperforms existing learning-based methods in terms of accuracy, generalization ability for 2D medical image registration, and also retains the efficiency advantages.</p

Two distinct flyways with different population trends of Bewick's Swan Cygnus columbianus bewickii in East Asia

Two of the most fundamental ecological questions about any species relate to where they occur and in what abundance. Here, we combine GPS telemetry data, survey data and expert knowledge for the first time to define two distinct flyways (the East Asian Continental and West Pacific flyways), migration routes and abundance for the Eastern population of Bewick’s Swan Cygnus columbianus bewickii. The Eastern population is the largest flyway population, supporting c. 77% of Bewick’s Swan numbers globally. GPS telemetry data showed that birds breeding in the Russian arctic from the Yamal Peninsula to c. 140°E (including the Lena and Yana Deltas), winter in the middle and lower reaches of the Yangtze River in China (which we label the “East Asian Continental flyway”). Bewick’s Swans breeding from the Indigirka River east to the Koluchin Bay winter in Japan, mostly in Niigata, Yamagata and Ishikawa Prefectures (the “West Pacific flyway”). There was no overlap in migration routes used by tagged individuals from the two flyways. Counts of Bewick’s Swans in the East Asian Continental flyway during the 21st century have shown wide between-year variations, reflecting incomplete coverage in earlier years. Bewick’s Swans in this flyway currently numbers c. 65,000 birds based on extensive wintering survey coverage, compared to c. 81,000 in the early 2000s, based on less complete coverage. Chinese-wintering swans now concentrate mainly (c. 80%) at Poyang Lake in Jiangxi Province and Hubei Lakes (mostly in Longgan Lake), compared to a more widespread distribution both within Poyang and throughout the Auhui Lakes in 2004 and 2005. In contrast, Bewick’s Swans of the West Pacific flyway now numbers c. 40,000, compared to just 542 in 1970. This population has shown no significant overall change since 2004, when it numbered c. 45,000 birds. Small numbers within this population probably also winter in South Korea. These results provide our first basic understanding of the winter distribution of Chinese- and Japanese-wintering Bewick’s Swans in relation to their breeding areas, confirming the need to coordinate future research and monitoring in the two flyways, as well as the need for more information on swans wintering in South Korea