Transformer Lesion Tracker

Evaluating lesion progression and treatment response via longitudinal lesion tracking plays a critical role in clinical practice. Automated approaches for this task are motivated by prohibitive labor costs and time consumption when lesion matching is done manually. Previous methods typically lack the integration of local and global information. In this work, we propose a transformer-based approach, termed Transformer Lesion Tracker (TLT). Specifically, we design a Cross Attention-based Transformer (CAT) to capture and combine both global and local information to enhance feature extraction. We also develop a Registration-based Anatomical Attention Module (RAAM) to introduce anatomical information to CAT so that it can focus on useful feature knowledge. A Sparse Selection Strategy (SSS) is presented for selecting features and reducing memory footprint in Transformer training. In addition, we use a global regression to further improve model performance. We conduct experiments on a public dataset to show the superiority of our method and find that our model performance has improved the average Euclidean center error by at least 14.3% (6mm vs. 7mm) compared with the state-of-the-art (SOTA). Code is available at https://github.com/TangWen920812/TLT.Comment: Accepted MICCAI 202

RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans

In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost. Nevertheless, the coarse resolution may lead to difficulties in medical diagnosis by either physicians or computer-aided diagnosis algorithms. Deep learning-based volumetric super-resolution (SR) methods are feasible ways to improve resolution, with convolutional neural networks (CNN) at their core. Despite recent progress, these methods are limited by inherent properties of convolution operators, which ignore content relevance and cannot effectively model long-range dependencies. In addition, most of the existing methods use pseudo-paired volumes for training and evaluation, where pseudo low-resolution (LR) volumes are generated by a simple degradation of their high-resolution (HR) counterparts. However, the domain gap between pseudo- and real-LR volumes leads to the poor performance of these methods in practice. In this paper, we build the first public real-paired dataset RPLHR-CT as a benchmark for volumetric SR, and provide baseline results by re-implementing four state-of-the-art CNN-based methods. Considering the inherent shortcoming of CNN, we also propose a transformer volumetric super-resolution network (TVSRN) based on attention mechanisms, dispensing with convolutions entirely. This is the first research to use a pure transformer for CT volumetric SR. The experimental results show that TVSRN significantly outperforms all baselines on both PSNR and SSIM. Moreover, the TVSRN method achieves a better trade-off between the image quality, the number of parameters, and the running time. Data and code are available at https://github.com/smilenaxx/RPLHR-CT.Comment: Accepted MICCAI 202

Response of Northern Populations of Black Spruce and Jack Pine to Southward Seed Transfers: Implications for Climate Change

A variety of responses to climate change have been reported for northern tree populations, primarily from tree-ring and satellite-based studies. Here we employ provenance data to examine growth and survival responses of northern populations (defined here as those occurring north of 52° N) of black spruce (Picea mariana) and jack pine (Pinus banksiana) to southward seed transfers. This space for time substitution affords insights into potential climate change responses by these important northern tree species. Based on previous work, we anticipated relatively flat response curves that peak at much warmer temperatures than those found at seed source origin. These expectations were generally met for growth-related responses, with peak growth associated with seed transfers to environments with mean annual temperatures 2.2 and 3.6 °C warmer than seed source origin for black spruce and jack pine, respectively. These findings imply that northern tree populations harbor a significant amount of resilience to climate warming. However, survival responses told a different story, with both species exhibiting reduced survival rates when moved to warmer and drier environments. Together with the growth-based results, these findings suggest that the warmer and drier conditions expected across much of northern Canada under climate change may reduce survival, but surviving trees may grow at a faster rate up until a certain magnitude of climate warming has been reached. We note that all relationships had high levels of unexplained variation, underlining the many factors that may influence provenance study outcomes and the challenges in predicting tree responses to climate change. Despite certain limitations, we feel that the provenance data employed here provide valuable insights into potential climate change outcomes for northern tree populations