The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.

MRI and associated contrast agent administration to visualize the vasculature prove valuable for planning surgical interventions and can reduce the operative time by helping to locate internal structures of interest for the operation. However, the visual representation of soft tissues deviates due to differences in body posture, in water and fat content, and in their gravitational displacement. In this paper, we present a novel approach for calculating deformations of abdominal fat tissue and vascular structures in MRI scans. The underlying elastic registration model is based on a current abdominal RGB-D scan as a surface-matching target. We demonstrate the pipeline on Dixon MRI and RGB-D scans acquired from ten patients with a diagnosis of breast cancer and a treatment plan including mastectomy. Results indicate that the proposed system enhances the surgeon's spatial perception of the abdominal vasculature and improves the accuracy of blood vessel locations shown in MRI scans.

The assessment of cerebrovascular disease benefits from the availability of different neuroimaging modalities, each providing different aspects to the vasculature and surrounding brain parenchyma. Segmented vasculature may improve cross-modal alignment but is difficult to annotate in sequences without vessel contrast. We propose a novel method to segment cerebral arteries in three non-angiographic sequences: T1w, T2w and PDw. Our method further predicts annotations for four anatomical regions, which can be used to mask specific parts of the vascular network or analyze the topology. For our experiments, we annotated arterial vessels and anatomical regions in 2,218 scans of the IXI dataset using a novel automatic method. We used the nnU-Net framework to train models in a 5-fold cross validation and to predict on a separate test-set. Our results suggest that vascular structures can be segmented and annotated in the examined MRI sequences with reasonable quality. The approach may potentially be used to study vascular diseases, when trained on pathological images. We share our ground-truth and models to encourage future experiments.

The treatment of burns is supported by accurately estimating measurements such as total body surface area burned (TBSA-b) and total body surface area (TBSA). Computing these values automatically can lead to faster decisions and reduce human error. Deep learning-based methods rely on big data to perform well in practical applications. This is especially true for burn medicine, where real data is scarce. In this paper, we present a pipeline for synthesizing diagnostic burn images and wound annotations from virtual 3D models. We demonstrate how to generate a heterogeneous dataset by combining such features as body shape, real skin and wound textures, background scenes, camera settings and illumination. The resulting images can be used for various deep-learning tasks such as wound detection, segmentation or classification. Solely with these synthetic images, we train models for burn wound segmentation and show that they learn distinctive features of burn wounds.

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup.