Automatic nuclei segmentation in H&E stained breast cancer histopathology images

The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8. © 2013 Veta et al

Retinal Artery/Vein Classification via Graph Cut Optimization

In many diseases with a cardiovascular component, the geometry of microvascular blood vessels changes. These changes are specific to arteries and veins, and can be studied in the microvasculature of the retina using retinal photography. To facilitate large-scale studies of artery/vein-specific changes in the retinal vasculature, automated classification of the vessels is required. Here we present a novel method for artery/vein classification based on local and contextual feature analysis of retinal vessels. For each vessel, local information in the form of a transverse intensity profile is extracted. Crossings and bifurcations of vessels provide contextual information. The local and contextual features are integrated into a non-submodular energy function, which is optimized exactly using graph cuts. The method was validated on a ground truth data set of 150 retinal fundus images, achieving an accuracy of 88.0% for all vessels and 94.0% for the six arteries and six veins with highest caliber in the image

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy

Objective: To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. Background: RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. Methods: Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. Results: The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. Conclusion: This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.</p

Nonrigid registration using a rigidity constraint

Nonrigid registration is a technique commonly used in the field of medical imaging. A drawback of most current nonrigid registration algorithms is that they model all tissue as being nonrigid. When a nonrigid registration is performed, the rigid objects in the image, such as bony structures or surgical instruments, may also transform nonrigidly. Other consequences are that tumour growth between follow-up images may be concealed, or that structures containing contrast material in one image and not in the other may be compressed by the registration algorithm. In this paper we propose a novel regularisation term, which is added to the cost function in order to penalise nonrigid deformations of rigid objects. This regularisation term can be used for any representation of the deformation field capable of modelling locally rigid deformations. By using a B-spline representation of the deformation field, a fast algorithm can be devised. We show on 2D synthetic data, on clinical CT slices, and on clinical DSA images, that the proposed rigidity constraint is successful, thus improving registration results

A comparison of acceleration techniques for nonrigid medical image registration

Mutual information based nonrigid registration of medical images is a popular approach. The coordinate mapping that relates the two images is found in an iterative optimisation procedure. In every iteration a computationally expensive evaluation of the mutual information's derivative is required. In this work two acceleration strategies are compared. The first technique aims at reducing the number of iterations, and, consequently, the number of derivative evaluations. The second technique reduces the computational costs per iteration by employing stochastic approximations of the derivatives. The performance of both methods is tested on an artificial registration problem, where the ground truth is known, and on a clinical problem involving low-dose CT scans and large deformations. The experiments show that the stochastic approximation approach is superior in terms of speed and robustness. However, more accurate solutions are obtained with the first technique. © Springer-Verlag Berlin Heidelberg 2006