Deep Learning Segmentation of Glomeruli on Kidney Donor Frozen Sections

Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. This work aims to develop deep learning (DL) models to quantify non-sclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution (n=123) and at external institutions (n=135) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (Ratio: 0.8:0.2) and external WSIs were used as an independent testing dataset. Non-sclerotic (n=22767) and sclerotic (n=1366) glomeruli were manually annotated by study pathologists on all WSIs. A 9-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of non-sclerotic and sclerotic glomeruli. DL-derived, manual segmentation and reported glomerular count (standard of care) were compared. Results: The average Dice Similarity Coefficient testing was 0.90 and 0.83. and the F1, Recall, and Precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for non-sclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. This work represents the first step toward new protocols for the evaluation of donor kidney biopsies.

Automated computational analysis reveals structural changes in the enteric nervous system of nNOS deficient mice

Neuronal nitric oxide synthase (nNOS) neurons play a fundamental role in inhibitory neurotransmission, within the enteric nervous system (ENS), and in the establishment of gut motility patterns. Clinically, loss or disruption of nNOS neurons has been shown in a range of enteric neuropathies. However, the effects of nNOS loss on the composition and structure of the ENS remain poorly understood. The aim of this study was to assess the structural and transcriptional consequences of loss of nNOS neurons within the murine ENS. Expression analysis demonstrated compensatory transcriptional upregulation of pan neuronal and inhibitory neuronal subtype targets within the Nos1−/− colon, compared to control C57BL/6J mice. Conventional confocal imaging; combined with novel machine learning approaches, and automated computational analysis, revealed increased interconnectivity within the Nos1−/− ENS, compared to age-matched control mice, with increases in network density, neural projections and neuronal branching. These findings provide the first direct evidence of structural and molecular remodelling of the ENS, upon loss of nNOS signalling. Further, we demonstrate the utility of machine learning approaches, and automated computational image analysis, in revealing previously undetected; yet potentially clinically relevant, changes in ENS structure which could provide improved understanding of pathological mechanisms across a host of enteric neuropathies.

Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma

Glioblastoma is the most common primary brain tumor. Standard therapy consists of maximum safe resection combined with adjuvant radiochemotherapy followed by chemotherapy with temozolomide, however prognosis is extremely poor. Assessment of the residual tumor after surgery and patient stratification into prognostic groups (i.e., by tumor volume) is currently hindered by the subjective evaluation of residual enhancement in medical images (magnetic resonance imaging [MRI]). Furthermore, objective evidence defining the optimal time to acquire the images is lacking. We analyzed 144 patients with glioblastoma, objectively quantified the enhancing residual tumor through computational image analysis and assessed the correlation with survival. Pathological enhancement thickness on post-surgical MRI correlated with survival (hazard ratio: 1.98, p < 0.001). The prognostic value of several imaging and clinical variables was analyzed individually and combined (radiomics AUC 0.71, p = 0.07; combined AUC 0.72, p < 0.001). Residual enhancement thickness and radiomics complemented clinical data for prognosis stratification in patients with glioblastoma. Significant results were only obtained for scans performed between 24 and 72 h after surgery, raising the possibility of confounding non-tumor enhancement in very early post-surgery MRI. Regarding the extent of resection, and in agreement with recent studies, the association between the measured tumor remnant and survival supports maximal safe resection whenever possible.