Automated Segmentation of Cells with IHC Membrane Staining

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysi

The residual STL volume as a metric to evaluate accuracy and reproducibility of anatomic models for 3D printing: application in the validation of 3D-printable models of maxillofacial bone from reduced radiation dose CT images.

BackgroundThe effects of reduced radiation dose CT for the generation of maxillofacial bone STL models for 3D printing is currently unknown. Images of two full-face transplantation patients scanned with non-contrast 320-detector row CT were reconstructed at fractions of the acquisition radiation dose using noise simulation software and both filtered back-projection (FBP) and Adaptive Iterative Dose Reduction 3D (AIDR3D). The maxillofacial bone STL model segmented with thresholding from AIDR3D images at 100 % dose was considered the reference. For all other dose/reconstruction method combinations, a "residual STL volume" was calculated as the topologic subtraction of the STL model derived from that dataset from the reference and correlated to radiation dose.ResultsThe residual volume decreased with increasing radiation dose and was lower for AIDR3D compared to FBP reconstructions at all doses. As a fraction of the reference STL volume, the residual volume decreased from 2.9 % (20 % dose) to 1.4 % (50 % dose) in patient 1, and from 4.1 % to 1.9 %, respectively in patient 2 for AIDR3D reconstructions. For FBP reconstructions it decreased from 3.3 % (20 % dose) to 1.0 % (100 % dose) in patient 1, and from 5.5 % to 1.6 %, respectively in patient 2. Its morphology resembled a thin shell on the osseous surface with average thickness <0.1 mm.ConclusionThe residual volume, a topological difference metric of STL models of tissue depicted in DICOM images supports that reduction of CT dose by up to 80 % of the clinical acquisition in conjunction with iterative reconstruction yields maxillofacial bone models accurate for 3D printing

Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound

Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions. Automation of this process in 3D Ultrasound image data is desirable, since manual delineations are time-consuming, challenging and observer-dependent. Convolutional neural networks have made improvements in computer vision and in medical image analysis. They have successfully been applied to segmentation tasks and were extended to work on volumetric data. In this paper we introduce a combined deep-learning based approach on volumetric segmentation in Ultrasound acquisitions with incorporation of prior knowledge about left atrial shape and imaging device. The results show, that including a shape prior helps the domain adaptation and the accuracy of segmentation is further increased with adversarial learning

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo