Methods and systems for determining a tumor volume from image data obtained from a functional scanner. The methods and systems can include identifying a portion within a region of interest corresponding to a liver that varies in intensity with its corresponding neighboring portion by a threshold. The method and systems can further include determining a volume of the portion without identifying a boundary of the portion. The portion can also be tracked over time. The image data can include a scan from a SPECT scanner.

A model dataset is generated based on first image data. The model dataset and second image data map at least a common part of an examination region at a second detail level. The model dataset and the second image data are pre-aligned at a first detail level below the second detail level based on first features that are mapped at the first detail level in the model dataset and the second image data and/or an acquisition geometry of the second image data. The model dataset and the second image data are registered at the second detail level based on second features that are mapped at the second detail level in the model dataset and the second image data. The second class of features is mappable at the second detail level or above. The registered second image data and/or the registered model dataset is provided.

The present disclosure deals with the quickening of MRI examinations. Subjects of the present disclosure are a method, a system, a computer program product, a use, a contrast agent for use and a kit.

A computed tomography (CT) image processing apparatus and a CT image processing method are provided. The CT image processing apparatus may generate a virtual monochromatic image (VMI) by applying a weight to each of first, second, and third images corresponding to three different energy ranges. The CT image processing apparatus may set a region of interest (ROI) on a CT image, determine a VMI at an energy level at which a CNR of the ROI is at a maximum among a plurality of VMIs, and display the determined VMI.

Distance estimation is optimized in virtual or augmented reality. A distance map of a surgical instrument to a region of interest is determined, at least at the beginning and when a position of the surgical instrument has changed. A render-image is rendered based on a medical 3D image and the position of the surgical instrument, at least at the beginning and when the position of the surgical instrument has changed. At least the region of interest and those parts of the surgical instrument positioned in the volume of the render-image are shown in the render-image. Based on the distance map, at least for a predefined area of the region of interest, visible, acoustic, and/or haptic distance-information is added.

Some embodiments include a method, comprising: receiving an image representing a branching structure; determining a starting feature of the branching structure; selecting a subregion of the image based on the starting feature; segmenting the branching structure in the subregion; generating a set of next features based on the segmented branching structure; and for each of the next features, repeating the selecting of the subregion based on the next feature, the segmenting of the branching structure, and the generating of the set of next features.

The present disclosure is related to systems and methods for image registration. The method includes obtaining a first image of a first modality associated with a subject and a second image of a second modality associated with the subject. The method includes determining a first region of interest (ROI) in the first image and a second ROI in the second image, wherein the first ROI and the second ROI correspond to a same region of the subject. The method includes registering the first ROI and the second ROI.

Provided are a pathological diagnosis support apparatus, an operation method for a pathological diagnosis support apparatus, and an operation program for a pathological diagnosis support apparatus, by which the detection accuracy of a fibrotic region can be improved. An RW control unit of the pathological diagnosis support apparatus acquires a sample image obtained by imaging a sample of a liver stained with Sirius red by reading it out from a storage device. A detection unit detects a fibrotic region of the liver by comparing, with a preset threshold value, a ratio between the pixel values of a G channel and an R channel among three color channels of RGB of the sample image. A derivation unit derives evaluation information indicating a degree of fibrosis of the liver based on the detected fibrotic region. A display control unit outputs the evaluation information by displaying an analysis result display screen including the evaluation information on a display.

Described herein are means for generating pre-trained models for nnU-Net through the use of improved transfer learning techniques, in which the pre-trained models are then utilized for the processing of medical imaging. According to a particular embodiment, there is a system specially configured for segmenting medical images, in which such a system includes: a memory to store instructions; a processor to execute the instructions stored in the memory; wherein the system is specially configured to: execute instructions via the processor for executing a pre-trained model from Models Genesis within a nnU-Net framework; execute instructions via the processor for learning generic anatomical patterns within the executing Models Genesis through self-supervised learning; execute instructions via the processor for transforming an original image using distortion and cutout-based methods; execute instructions via the processor for learning the reconstruction of the original image from the transformed image using an encoder-decoder architecture of the nnU-Net framework to identify the generic anatomical representation from the transformed image by recovering the original image; and wherein architecture determined by the nnU-Net framework is utilized with Models Genesis and is trained to minimize the L2 distance between the prediction and ground truth. Other related embodiments are disclosed.

A method to suppresses speckle noise in medical ultrasound images includes ultrasound envelope image matrix A formed from the medical ultrasound images and segmented into overlapping segments, to form a sub-matrix B for each overlapping segment. A Hermitian covariance matrix C is formulated from column vectors Z. A global covariance matrix G is formed by averaging the C. A Lanczos decomposition is applied to the G to generate an orthonormal vector matrix composed of orthonormal vectors. A tridiagonal matrix H is generated. The orthonormal vectors are sorted based on magnitude of each column. An orthogonal projection matrix P.sub.orth is formed based on the orthonormal vectors. An estimated vector signal {circumflex over (Z)} is obtained by projecting Z by P.sub.orth. An estimated despeckled segment is formed from the {circumflex over (Z)}. An estimated despeckled ultrasound image is reconstructed by averaging each pixel by the number of segment updates.