Weekly changes in ventilation response for photon and proton lung cancer patients during radiotherapy.

Purpose: Conformal dose distributions in proton radiotherapy promise to reduce normal tissue toxicity such as radiation-induced pneumonitis, but this has not been fully realized in clinical trials. To further investigate dose and toxicity, we employ voxel-based normal tissue evaluation techniques such as ventilation maps throughout treatment. We hypothesize that ventilation change after 1 week of treatment (WK1) predicts for ventilation change at the end of treatment (EOT).

Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment.

Background And Purpose: Accurate delineation of orodental structures on radiotherapy computed tomography (CT) images is essential for dosimetric assessment and dental decisions. We propose a deep-learning (DL) auto-segmentation framework for individual teeth and mandible/maxilla sub-volumes aligned with the ClinRad osteoradionecrosis staging system.

Materials And Methods: Mandible and maxilla sub-volumes were manually defined on simulation CT images from 60 clinical cases, differentiating alveolar from basal regions; teeth were labelled individually.

Quantifying changes in ventilation for photon- and proton-treated patients according to dose and pneumonitis development.

Treatment of non-small cell lung cancer with proton therapy allows for delivery of high radiation dose while sparing critical normal tissue structures compared to conventional photon therapy. However, there are currently no established clinical endpoints to study the biological effect between these radiation modalities. Computed tomography (CT) imaging allows for extraction of ventilation through deformable image registration of 4DCTs and may provide information on imaging-based functional changes in the lung.

Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend.

Fréchet Inception Distance (FID) is a widely used metric for assessing synthetic image quality. It relies on an ImageNet-based feature extractor, making its applicability to medical imaging unclear. A recent trend is to adapt FID to medical imaging through feature extractors trained on medical images.

Image-based Mandibular and Maxillary Parcellation and Annotation using Computer Tomography (IMPACT): A Deep Learning-based Clinical Tool for Orodental Dose Estimation and Osteoradionecrosis Assessment.

Background: Accurate delineation of orodental structures on radiotherapy CT images is essential for dosimetric assessments and dental decisions. We propose a deep-learning auto-segmentation framework for individual teeth and mandible/maxilla sub-volumes aligned with the ClinRad ORN staging system.

Methods: Mandible and maxilla sub-volumes were manually defined, differentiating between alveolar and basal regions, and teeth were labelled individually.

Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.

Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text].

Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation.

Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models tend to perform well in most instances, which could exacerbate automation bias. Therefore, detecting out-of-distribution images at inference is critical to warn the clinicians that the model likely failed.