Abstract: The present disclosure, in some embodiments, relates to a method. The method includes accessing one or more digitized pathology images of a cell cluster area comprising epithelial cells obtained from a bile duct of a patient having a bile duct stricture. The cell cluster area is segmented to identify segmented nuclei and non-nuclei regions. A plurality of texture features are extracted from the segmented nuclei and the non-nuclei regions. A plurality of nuclear shape features are extracted from the segmented nuclei. The plurality of nuclear shape features and the plurality of texture features are provided to a machine learning model configured to generate a cytological diagnosis of the epithelial cells within the cell cluster area.

Abstract: Embodiments discussed herein facilitate determination of cancer stages based at least in part on shape, size, and/or texture features of cancer nuclei. One example embodiment is a method, comprising: accessing at least a portion of a digital whole slide image (WSI) comprising a tumor; segmenting (e.g., via a first deep learning model) the tumor on the at least the portion of the digital WSI; segmenting (e.g., via a second deep learning model) cancer nuclei in the segmented tumor; extracting one or more features from the segmented cancer nuclei; providing the one or more features extracted from the segmented cancer nuclei to a trained machine learning model; and receiving, from the machine learning model, an indication of a cancer stage of the tumor.

Abstract: Embodiments discussed herein facilitate determination of one of a probability of prostate cancer recurrence-free survival or a risk factor associated with prostate cancer based on intra-tumor stromal morphology. Example embodiments can perform operations comprising: accessing a digitized histological image of a prostate of a patient, wherein the histological image comprises a region of interest associated with prostate cancer; identifying nuclei of intra-tumoral stromal cells within the region of interest; extracting, for the region of interest of the digitized histological image, one or more features describing the structure of the intra-tumoral stromal cells; and generating, via a model based at least on the one or more features, one of a probability of prostate cancer recurrence-free survival or a risk score associated with prostate cancer for the patient based at least on the extracted one or more features.

Abstract: The present disclosure relates to a method. The method includes accessing data having one or more segmented images identifying one or more lesions and/or a plurality of blood vessels associated with the lesions. Respective ones of the blood vessels correspond to one or more centerlines and one or more constituent branches associated with the one or more lesions. The one or more segmented images are derived from one or more radiological images of a patient having cancer. One or more vascular radiology features are extracted using the centerlines, the constituent branches, and the one or more lesions. The one or more vascular radiology features relate to a quantification of the plurality of blood vessels or a shape of the plurality of blood vessels. The one or more vascular radiology features are used to determine a medical prediction associated with an outcome of the patient to cyclin-dependent kinase (CDK) inhibitor therapy.

Abstract: Embodiments predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BCa) from pre-treatment dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

Abstract: The present disclosure relates to a method that provides a pre-treatment image of a region of tissue to a deep learning model. The pre-treatment image includes at least one lesion. The deep learning model has been trained to generate a first prediction as to whether the region of tissue will respond to medical treatment. A set of radiomic features are extracted from the pre-treatment image and are provided to a machine learning model. The machine learning model has been trained to generate a second prediction as to whether the region of tissue will respond to the medical treatment based on the set of radiomic features. The deep learning model is controlled to generate the first prediction and the machine learning model is controlled to generate the second prediction. A classification of the region of tissue as a responder or non-responder is generated based on the first and second prediction.

Abstract: Embodiments include accessing an image of a region of tissue demonstrating cancerous pathology; detecting a plurality of cells represented in the image; segmenting a cellular nucleus of a first member of the plurality of cells and a cellular nucleus of at least one second, different member of the plurality of cells; extracting a set of nuclear morphology features from the plurality of cells; constructing a feature driven local cell graph (FeDeG) based on the set of nuclear morphology features and a spatial relationship between the cellular nuclei using a mean-shift clustering approach; computing a set of FeDeG features based on the FeDeG; providing the FeDeG features to a machine learning classifier; receiving, from the machine learning classifier, a classification of the region of tissue as a long-term or a short-term survivor, based, at least in part, on the set of FeDeG features; and displaying the classification.

Abstract: The present disclosure relates to a method. The method may be performed by accessing data derived from one or more routine clinical medical imaging scans including a lesion in which the lesion and associated vasculature are segmented in a three-dimensional segmentation. At least two features are extracted from the three-dimensional segmentation of the associated vasculature. The at least two features include at least one feature indicative of a morphology of the associated vasculature or a portion thereof, and at least one feature indicative of a function of the associated vasculature or a portion thereof. The at least two features, and/or one or more statistics of the at least two features, are provided to a machine learning model trained to make a prediction concerning the lesion. The prediction concerning the lesion is received from the machine learning model.

Abstract: Embodiments discussed herein facilitate determination of a response to treatment and/or a prognosis for a tumor based at least in part on features of tumor-associated vasculature (TAV). One example embodiment is a method, comprising: accessing a medical imaging scan of a tumor, wherein the tumor is segmented on the medical imaging scan; segmenting tumor-associated vasculature (TAV) associated with the tumor based on the medical imaging scan; extracting one or more features from the TAV; providing the one or more features extracted from the TAV to a trained machine learning model; and receiving, from the machine learning model, one of a predicted response to a treatment for the tumor or a prognosis for the tumor.

Abstract: The degree of differentiation of a cell in tissue is precisely determined. An estimating device (1) includes: a binarizing section (41) configured to generate binarized images from an image obtained by capturing an image of tissue; a Betti number calculating section (42) configured to calculate, for each binarized image, (i) the number of hole-shaped regions (b1) each surrounded by pixels of a first pixel value and each composed of pixels of a second pixel value, (ii) the number of connected regions each composed of the pixels of the first pixel value connected together, and (iii) a ratio (R) between (i) and (ii); a statistic calculating section (43) configured to calculate statistics of the calculated numbers (b1, b0) and ratio (R); and an estimating section (44) configured to feed input data including the calculated statistics to a trained estimating model to output the degree of differentiation of the cell in tissue.

Abstract: Embodiments discussed herein facilitate determination of whether lesions are benign or malignant. One example embodiment is a method, comprising: accessing medical imaging scan(s) that are each associated with distinct angle(s) and each comprise a segmented region of interest (ROI) of that medical imaging scan comprising a lesion associated with a first region and a second region; providing the first region(s) of the medical imaging scan(s) to trained first deep learning (DL) model(s) of an ensemble and the second region(s) of the medical imaging scan(s) to trained second DL model(s) of the ensemble; and receiving, from the ensemble of DL models, an indication of whether the lesion is a benign architectural distortion (AD) or a malignant AD.

Abstract: Embodiments discussed herein facilitate training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors. One example embodiment can extract radiomic features from a tumor and a peri-tumoral region; provide the intra-tumoral and peri-tumoral features to two separate machine learning models; provide the segmented tumor and peri-tumoral region to two separate deep learning models; receive predicted prognoses from each of the machine learning models and each of the deep learning models; provide the predicted prognoses to a combined machine learning model; and receive a combined predicted prognosis for the tumor from the combined machine learning model.

Abstract: The present disclosure, in some embodiments, relates to a non-transitory computer-readable medium storing computer-executable instructions. The computer readable medium is configured to cause a processor to access an image volume of a rectum comprising a rectal tumor. A forward mapping is generated based on non-rigidly registering a healthy rectal atlas to the image volume. The forward mapping is inverted to generate an inverse mapping from the image volume to the healthy rectal atlas. Based on the inverse mapping, a plurality of deformation vectors, associated with a deformation within a rectal wall of the rectum, are determined. Magnitude based deformation features and orientation based deformation features are computed from the plurality of deformation vectors. One or more of the magnitude based deformation features and one or more of the orientation based deformation features are utilized to determine a response of a patient to a chemoradiation treatment.

Abstract: The present disclosure, in some embodiments, relates to a method. The method includes using a first machine learning model to generate a first medical prediction associated with a lesion in a medical scan using one or more intra-lesional radiomic features associated with the lesion and the one or more peri-lesional radiomic features associated with a peri-lesional region around the lesion. A second machine learning model is used to generate a second medical prediction associated with the lesion using one or more pathomic features associated with the lesion. A combined medical prediction associated with the lesion is generated using the first medical prediction and the second medical prediction as inputs to a third model.

Abstract: Embodiments discussed herein facilitate building and/or employing model(s) for determining tumor prognoses based on a combination of radiomic features and pathomic features. One example embodiment can perform actions comprising: providing, to a first machine learning model, at least one of: one or more intra-tumoral radiomic features associated with a tumor or one or more peri-tumoral radiomic features associated with a peri-tumoral region around the tumor; receiving a first predicted prognosis associated with the tumor from the first machine learning model; providing, to a second machine learning model, one or more pathomic features associated with the tumor; receiving a second predicted prognosis associated with the tumor from the second machine learning model; and generating a combined prognosis associated with the tumor based on the first predicted prognosis and the second predicted prognosis.

Abstract: The present disclosure in some embodiments relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including obtaining one or more digitized endomyocardial biopsy (EMB) images from a patient having had a heart transplant; extracting a plurality of histological features from the one or more digitized EMB images; and applying a machine learning predictive model to operate on the plurality of histological features to generate a prediction for the patient. The prediction includes a grade or a clinical trajectory associated with the patient.

Abstract: Systems, methods, and apparatus are provided for generating an atlas image of a branched structure and predicting a likelihood of success of certain treatments based on the atlas image. In one example, a method includes registering a plurality of images of instances of a branched structure to generate an aligned image for a cohort, wherein the branched structure comprises a central structure and at least one primary branch connected to the central structure; for each primary branch of the branched structure, iteratively registering respective portions of a plurality of images containing the primary branch to generate an aligned image portion of the primary branch; and applying a control grid of the aligned image portion of the primary branch to respective image portions containing the central structure and the other primary branches prior to iteratively registering a next primary branch; and generating an atlas image for the cohort based on the aligned image portions.

Abstract: Embodiments discussed herein facilitate segmentation of histological primitives from stained histology of renal biopsies via deep learning and/or training deep learning model(s) to perform such segmentation. One example embodiment is configured to access a first histological image of a renal biopsy comprising a first type of histological primitives, wherein the first histological image is stained with a first type of stain; provide the first histological image to a first deep learning model trained based on the first type of histological primitive and the first type of stain; and receive a first output image from the first deep learning model, wherein the first type of histological primitives is segmented in the first output image.

Abstract: Embodiments discussed herein facilitate building and/or employing a clinical-radiomics score for determining tumor prognoses based on a combination of a radiomics risk score generated by a machine learning model and clinico-pathological factors. One example embodiment can perform actions comprising: accessing a medical imaging scan of a tumor; segmenting a peri-tumoral region around the tumor; extracting one or more intra-tumoral radiomic features from the tumor and one or more peri-tumoral radiomic features from the peri-tumoral region; providing the one or more intra-tumoral radiomic features and the one or more peri-tumoral radiomic features to a trained machine learning model; receiving a radiomic risk score (RRS) associated with the tumor from the machine learning model; determining a clinical-radiomics score based on the RRS and one or more clinico-pathological factors; and generating a prognosis for the tumor based on the clinical-radiomics score.

Abstract: The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations. The operations include extracting one or more image characterization metrics from respective ones of a plurality of digitized images within an imaging data set. The plurality of digitized images have batch effects. The operations further include identifying a plurality of batch effect groups of the digitized images using the one or more image characterization metrics, and dividing the plurality of batch effect groups between a training set and/or a validation set. The training set and/or the validation set include some of the plurality of digitized images associated with respective ones of the plurality of batch effect groups.