Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging

In order to solve the problems of low confidence and poor correlation of feature point pairs between images due to different imaging principles and significant modal differences in the heterogenic image alignment task. This paper proposes a heterologous image-matching method based on a multi-level screening strategy based on the binocular camera's imaging principle and relevant a priori knowledge of image alignment. Firstly, we remove the apparent error points in the feature point pairs according to the feature points' relative position and main direction through the feature point pair screening strategy based on the search area and the feature point pair screening strategy based on the slope consistency. Then, the final screening optimization of feature points is carried out by improving the RANSAC algorithm to improve the quality of image alignment further. Finally, to verify the performance of the alignment algorithm proposed in this paper, the alignment results of this paper's method on several heterogeneous image datasets are compared. The experimental results show that compared with other algorithms, the alignment algorithm proposed in this paper has significant advantages in accuracy, stability, and matching efficiency performance indexes.

A Heterogeneous Image-Matching Algorithm Based on a Multi-Level Screening Strategy

Abstract Background Patients may undergo anatomical changes during radiotherapy, leading to an underdosing of the target or overdosing of the organs at risk (OARs). Purpose This study developed a deep‐learning method to predict the tumor response of patients with nasopharyngeal carcinoma (NPC) during treatment. This method can predict the anatomical changes of a patient. Methods The participants included 230 patients with NPC. The data included planning computed tomography (pCT) and routine cone‐beam CT (CBCT) images. The CBCT image quality was improved to the CT level using an advanced method. A long short‐term memory network‐generative adversarial network (LSTM‐GAN) is proposed, which can harness the forecasting ability of LSTM and the generation ability of GAN. Four models were trained to predict the anatomical changes that occurred in weeks 3–6 and named LSTM‐GAN‐week 3 to LSTM‐GAN‐week 6. The pCT and CBCT were used as input, and the tumor target volumes (TVs) and OARs were delineated on the predicted and real images (ground truth). Finally, the models were evaluated using contours and dosimetry parameters. Results The proposed method predicted the anatomical changes, with a dice similarity coefficient above 0.94 and 0.90 for the TVs and surrounding OARs, respectively. The dosimetry parameters were close between the prediction and ground truth. The deviations in the prescription, minimum, and maximum doses of the tumor targets were below 0.5 Gy. For serial organs (brain stem and spinal cord), the deviations in the maximum dose were below 0.6 Gy. For parallel organs (bilateral parotid glands), the deviations in the mean dose were below 0.8 Gy. Conclusion The proposed method can predict the tumor response to radiotherapy in the future such that adaptation can be scheduled on time. This study provides a proactive mechanism for planning adaptation, which can enable personalized treatment and save clinical time by anticipating and preparing for treatment strategy adjustments. 

Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy

We introduce"PatchMorph,"an new stochastic deep learning algorithm tailored for unsupervised 3D brain image registration. Unlike other methods, our method uses compact patches of a constant small size to derive solutions that can combine global transformations with local deformations. This approach minimizes the memory footprint of the GPU during training, but also enables us to operate on numerous amounts of randomly overlapping small patches during inference to mitigate image and patch boundary problems. PatchMorph adeptly handles world coordinate transformations between two input images, accommodating variances in attributes such as spacing, array sizes, and orientations. The spatial resolution of patches transitions from coarse to fine, addressing both global and local attributes essential for aligning the images. Each patch offers a unique perspective, together converging towards a comprehensive solution. Experiments on human T1 MRI brain images and marmoset brain images from serial 2-photon tomography affirm PatchMorph's superior performance.

PatchMorph: A Stochastic Deep Learning Approach for Unsupervised 3D Brain Image Registration with Small Patches

Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and most commonly in medical imaging. Deep Learning based techniques have been applied successfully to tackle various complex medical image processing problems, including medical image registration. Over the years, several image registration techniques have been proposed using deep learning. Deformable image registration techniques such as Voxelmorph have been successful in capturing finer changes and providing smoother deformations. However, Voxelmorph, as well as ICNet and FIRE, do not explicitly encode global dependencies (i.e. the overall anatomical view of the supplied image) and therefore can not track large deformations. In order to tackle the aforementioned problems, this paper extends the Voxelmorph approach in three different ways. To improve the performance in case of small as well as large deformations, supervision of the model at different resolutions have been integrated using a multi-scale UNet. To support the network to learn and encode the minute structural co-relations of the given image-pairs, a self-constructing graph network (SCGNet) has been used as the latent of the multi-scale UNet - which can improve the learning process of the model and help the model to generalise better. And finally, to make the deformations inverse-consistent, cycle consistency loss has been employed. On the task of registration of brain MRIs, the proposed method achieved significant improvements over ANTs and VoxelMorph, obtaining a Dice score of 0.8013$\pm$0.0243 for intramodal and 0.6211$\pm$0.0309 for intermodal, while VoxelMorph achieved 0.7747$\pm$0.0260 and 0.6071$\pm$0.0510, respectively.

MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent

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MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent