AIMC Topic: Image Processing, Computer-Assisted

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Automated volume measurement of abdominal adipose tissue from entire abdominal cavity in Dixon MR images using deep learning.

Radiological physics and technology
The purpose of this study was to realize an automated volume measurement of abdominal adipose tissue from the entire abdominal cavity in Dixon magnetic resonance (MR) images using deep learning. Our algorithm involves a combination of extraction of t...

Semi-supervised learning for topographic map analysis over time: a study of bridge segmentation.

Scientific reports
Geographical research using historical maps has progressed considerably as the digitalization of topological maps across years provides valuable data and the advancement of AI machine learning models provides powerful analytic tools. Nevertheless, an...

Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation.

Medical physics
BACKGROUND: Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustm...

Low-latency label-free image-activated cell sorting using fast deep learning and AI inferencing.

Biosensors & bioelectronics
Classification and sorting of cells using image-activated cell sorting (IACS) systems can bring significant insight to biomedical sciences. Incorporating deep learning algorithms into IACS enables cell classification and isolation based on complex an...

Multimodal medical image fusion algorithm based on pulse coupled neural networks and nonsubsampled contourlet transform.

Medical & biological engineering & computing
Combining two medical images from different modalities is more helpful for using the resulting image in the healthcare field. Medical image fusion means combining two or more images coming from multiple sensors. This technology obtains an output imag...

Cellpose 2.0: how to train your own model.

Nature methods
Pretrained neural network models for biological segmentation can provide good out-of-the-box results for many image types. However, such models do not allow users to adapt the segmentation style to their specific needs and can perform suboptimally fo...

Fine-Grained Image Analysis With Deep Learning: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate catego...

Self-Supervised Human Detection and Segmentation via Background Inpainting.

IEEE transactions on pattern analysis and machine intelligence
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibiti...

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends.

IEEE transactions on pattern analysis and machine intelligence
High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imag...

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology.

IEEE transactions on pattern analysis and machine intelligence
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the di...