Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis.

A system for analysis of histopathology data within a pharmaceutical R&D environment has been developed with the intention of enabling interdisciplinary collaboration. State-of-the-art AI tools have been deployed as easy-to-use self-service modules within an open-source whole slide image viewing platform, so that non-data scientist users (e.g., clinicians) can utilize and evaluate pre-trained algorithms and retrieve quantitative results. The outputs of analysis are automatically cataloged in the database to track data provenance and can be viewed interactively on the slide as annotations or heatmaps. Commonly used models for analysis of whole slide images including segmentation, extraction of hand-engineered features for segmented regions, and slide-level classification using multi-instance learning are included and new models can be added as needed. The source code that supports running inference with these models internally is backed up by a robust CI/CD pipeline to ensure model versioning, robust testing, and seamless deployment of the latest models. Examples of the use of this system in a pharmaceutical development workflow include glomeruli segmentation, enumeration of podocyte count from WT-1 immuno-histochemistry, measurement of beta-1 integrin target engagement from immunofluorescence, digital glomerular phenotyping from periodic acid-Schiff histology, PD-L1 score prediction using multi-instance learning, and the deployment of the open-source Segment Anything model to speed up annotation.

Retrospective fractional dose reduction in Tc-99m cardiac perfusion SPECT/CT patients: A human and model observer study.

BACKGROUND: In the ongoing efforts to reduce cardiac perfusion dose (injected radioactivity) for conventional SPECT/CT systems, we performed a human observer study to confirm our clinical model observer findings that iterative reconstruction employing OSEM (ordered-subset expectation-maximization) at 25% of the full dose (quarter-dose) has a similar performance for detection of hybrid cardiac perfusion defects as FBP at full dose. METHODS: One hundred and sixty-six patients, who underwent routine rest-stress Tc-99m sestamibi cardiac perfusion SPECT/CT imaging and clinically read as normally perfused, were included in the study. Ground truth was established by the normal read and the insertion of hybrid defects. In addition to the reconstruction of the 25% of full-dose data using OSEM with attenuation (AC), scatter (SC), and spatial resolution correction (RC), FBP and OSEM (with AC, SC, and RC) both at full dose (100%) were done. Both human observer and clinical model observer confidence scores were obtained to generate receiver operating characteristics (ROC) curves in a task-based image quality assessment. RESULTS: Average human observer AUC (area under the ROC curve) values of 0.725, 0.876, and 0.890 were obtained for FBP at full dose, OSEM at 25% of full dose, and OSEM at full dose, respectively. Both OSEM strategies were significantly better than FBP with P values of 0.003 and 0.01 respectively, while no significant difference was recorded between OSEM methods (P = 0.48). The clinical model observer results were 0.791, 0.822, and 0.879, respectively, for the same patient cases and processing strategies used in the human observer study. CONCLUSIONS: Cardiac perfusion SPECT/CT using OSEM reconstruction at 25% of full dose has AUCs larger than FBP and closer to those of full-dose OSEM when read by human observers, potentially replacing the higher dose studies during clinical reading.

Improving Diagnostic Accuracy in Low-Dose SPECT Myocardial Perfusion Imaging With Convolutional Denoising Networks.

Lowering the administered dose in SPECT myocardial perfusion imaging (MPI) has become an important clinical problem. In this study we investigate the potential benefit of applying a deep learning (DL) approach for suppressing the elevated imaging noise in low-dose SPECT-MPI studies. We adopt a supervised learning approach to train a neural network by using image pairs obtained from full-dose (target) and low-dose (input) acquisitions of the same patients. In the experiments, we made use of acquisitions from 1,052 subjects and demonstrated the approach for two commonly used reconstruction methods in clinical SPECT-MPI: 1) filtered backprojection (FBP), and 2) ordered-subsets expectation-maximization (OSEM) with corrections for attenuation, scatter and resolution. We evaluated the DL output for the clinical task of perfusion-defect detection at a number of successively reduced dose levels (1/2, 1/4, 1/8, 1/16 of full dose). The results indicate that the proposed DL approach can achieve substantial noise reduction and lead to improvement in the diagnostic accuracy of low-dose data. In particular, at 1/2 dose, DL yielded an area-under-the-ROC-curve (AUC) of 0.799, which is nearly identical to the AUC = 0.801 obtained by OSEM at full-dose ( p -value = 0.73); similar results were also obtained for FBP reconstruction. Moreover, even at 1/8 dose, DL achieved AUC = 0.770 for OSEM, which is above the AUC = 0.755 obtained at full-dose by FBP. These results indicate that, compared to conventional reconstruction filtering, DL denoising can allow for additional dose reduction without sacrificing the diagnostic accuracy in SPECT-MPI.

Improving perfusion defect detection with respiratory motion correction in cardiac SPECT at standard and reduced doses.

BACKGROUND: In cardiac SPECT perfusion imaging, respiratory motion can cause non-uniform blurring in the reconstructed myocardium. We investigate the potential benefit of respiratory correction with respiratory-binned acquisitions, both at standard dose and at reduced dose, for defect detection and for left ventricular (LV) wall resolution. METHODS: We applied two reconstruction methods for respiratory motion correction: post-reconstruction motion correction (PMC) and motion-compensated reconstruction (MCR), and compared with reconstruction without motion correction (Non-MC). We quantified the presence of perfusion defects in reconstructed images by using the total perfusion deficit (TPD) scores and conducted receiver-operating-characteristic (ROC) studies using TPD. We quantified the LV spatial resolution by using the FWHM of its cross-sectional intensity profile. RESULTS: The values in the area-under-the-ROC-curve (AUC) achieved by MCR, PMC, and Non-MC at standard dose were 0.835, 0.830, and 0.798, respectively. Similar AUC improvements were also obtained by MCR and PMC over Non-MC at 50%, 25%, and 12.5% of full dose. Improvements in LV resolution were also observed with motion correction. CONCLUSIONS: Respiratory-binned acquisitions can improve perfusion-defect detection accuracy over traditional reconstruction both at standard dose and at reduced dose. Motion correction may contribute to achieving further dose reduction while maintaining the diagnostic accuracy of traditional acquisitions.