A neural network for glomerulus classification based on histological images of kidney biopsy.

BACKGROUND: Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. RESULTS: We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). CONCLUSIONS: Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.

A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images.

BACKGROUND: The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. METHODS: Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. RESULTS: Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. CONCLUSION: The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

Characterization of adrenal lesions using MDCT wash-out parameters: diagnostic accuracy of several combinations of intermediate and delayed phases.

PURPOSE: To evaluate the diagnostic accuracy of wash-out parameters calculated using multiple intermediate and delayed phases. MATERIALS AND METHODS: This prospective study had institutional review board approval and informed consent was obtained from all patients. Between January 2012 and October 2016, 108 consecutive oncologic patients (59 males, 49 females, mean age 52.6 years; 129 diagnosed lesions) underwent multiphasic CT protocol including unenhanced (UE), arterial (AE), portal (PE), 5-min (DE-5) and the 15-min (DE-15) delayed phases of adrenal glands. All images were randomly reviewed in consensus by two radiologists experienced in abdominal CT, unaware of clinical or pathologic data. Location, size and density were recorded. Absolute wash-out, percentage wash-out (PWO) and percentage enhancement wash-out ratio were calculated. The thresholds yielding the best accuracy in differentiating adenomas from nonadenomas were retrospectively determined on the basis of ROC curves. The corresponding diagnostic accuracy values were calculated. Paired sample t test was used to assess differences among imaging parameters within subgroups. Student t test was applied to compare lesions between independent subgroups. p values ≤ 0.05 were considered significant. RESULTS: The final diagnosis included 82 adenomas (62 lipid-rich and 20 lipid-poor) and 47 nonadenomas (42 metastases, 3 pheochromocytomas, 2 carcinomas). All the 62 lipid-rich adenomas were correctly diagnosed as benign lesions on the basis of their UE attenuation < 10 HU. The PEAK attenuation was achieved during AE phase for 51/129 lesions (39.5%) and at the time of PE phase in 78/129 lesions (60.5%). The best overall accuracy in diagnosing adenomas (97.6%; 126/129 lesions correctly diagnosed) was obtained using 40% threshold for calculating PWO from PEAK to DE-15 scan. CONCLUSIONS: If only an intermediate phase is available, the 15-min delayed scan should be acquired to avoid any drop in diagnostic accuracy. The availability of two intermediate phase may be used to easy CT schedule by obviating the need to acquire a longer delayed phase.

Familial hypocalciuric hypercalcemia revealed by chondrocalcinosis.

BACKGROUND: Calcium pyrophosphate dihydrate crystal deposition disease (CPPD-CDD) has been associated to hypercalcemia. Familial hypocalciuric hypercalcemia (FHH) is a rare but important consideration in the differential diagnosis of hypercalcemia. This autosomal dominantly inherited condition is characterized by elevated plasma calcium levels, relative or absolute hypocalciuria, and normal to moderately elevated plasma PTH level. The disease is caused by inactivating mutations in the calcium-sensing receptor gene. CASE REPORT: We describe a 77-year-old Italian man with arthritis secondary to CPPD-CDD and hypercalcemia. Clinical and biochemical data (s-Ca: 2.94 mmol/L; PTH: 5.9 pmol/L; 24 h urinary calcium: 69.6 mg; calcium/creatinine clearance: 0.004) suggested the diagnosis of FHH. Mild hypocalciuric hypercalcemia was also found in five of seven relatives confirming the diagnosis, of these one showed chondrocalcinosis. CONCLUSIONS: It is important to screen for FHH using fractional urinary excretion of calcium in subjects with CPPD-CDD associated to hypercalcemia, this approach may prevent unnecessary parathyroidectomy.