This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development. Model performance was assessed using Dice similarity coefficient (DSC) and Bland-Altman analysis. External validation in 20 patients from outside institutions demonstrated a DSC of 0.98 (IQR, 0.97-0.99) and a Bland-Altman difference of 2.6% (95% CI: 1.0%, 4.1%). Prospective validation in 53 patients demonstrated a DSC of 0.97 (IQR, 0.94-0.98) and a Bland-Altman difference of 3.6% (95% CI: 2.0%, 5.2%). Last, the efficiency of model-assisted annotation was evaluated on the first 50% of prospective cases (n = 28), with a 51% mean reduction in contouring time (P , .001), from 1724 seconds (95% CI: 1373, 2075) to 723 seconds (95% CI: 555, 892). In conclusion, our deployed artificial intelligence pipeline accurately performs automated segmentation for TKV estimation of polycystic kidneys and reduces expert contouring time.ClinicalTrials.gov identification no.: NCT00792155
Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability.
BackgroundTotal kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates.PurposeTo improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1‐weighted, T2‐weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements.Study TypeRetrospective training, prospective testing.SubjectsThree hundred ninety‐seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility.Field Strength/SequenceT2‐weighted single‐shot fast spin echo (T2), SSFP, and T1‐weighted 3D spoiled gradient echo (T1) at 1.5 and 3T.Assessment2D U‐net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model‐assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1–3 weeks to assess reproducibility. Manual and model‐assisted segmentation times were recorded.Statistical TestsBland–Altman, Schapiro–Wilk (normality assessment), Pearson's chi‐squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P‐value < 0.05 was considered statistically significant.ResultsIn 17 ADPKD subjects, model‐assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model‐assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model‐assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier.Data ConclusionMeasuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model‐assisted segmentations and can improve TKV measurement reproducibility more than 2‐fold in ADPKD.Evidence Level2Technical EfficacyStage 1
Cell migration is a complex, tightly regulated multistep process in which cytoskeletal reorganization and focal adhesion redistribution play a central role. Core to both individual and collective migration is the persistent random walk, which is characterized by random force generation and resistance to directional change. We first discuss a model that describes the stochastic movement of ECs and characterizes EC persistence in wound healing. To that end, we pharmacologically disrupted cytoskeletal dynamics, cytochalasin D for actin and nocodazole for tubulin, to understand its contributions to cell morphology, stiffness, and motility. As such, the use of Atomic Force Microscopy (AFM) enabled us to probe the topography and stiffness of ECs, while time lapse microscopy provided observations in wound healing models. Our results suggest that actin and tubulin dynamics contribute to EC shape, compressive moduli, and directional organization in collective migration. Insights from the model and time lapse experiment suggest that EC speed and persistence are directionally organized in wound healing. Pharmacological disruptions suggest that actin and tubulin dynamics play a role in collective migration. Current insights from both the model and experiment represent an important step in understanding the biomechanics of EC migration as a therapeutic target.
Purpose: To develop and integrate interactive features with automatic methods for accurate liver cyst segmentation in patients with autosomal dominant polycystic kidney and liver disease (ADPKD). Methods: SmartClick and antiSmartClick were developed using iterative region growth guided by spatial and intensity connections and were integrated with automated level set (LS) segmentation and graphical user interface, forming an intelligent rapid interactive segmentation (IRIS) tool. IRIS and LS segmentations of liver cysts on T2 weighted images of patients with ADPKD (n = 17) were compared with manual segmentation as ground truth (GT). Results: Compared to manual GT, IRIS reduced the segmentation time by more than 10-fold. Compared to automated LS, IRIS reduced the mean liver cyst volume error from 42.22% to 13.44% (p < 0.001). IRIS segmentation agreed well with manual GT (79% dice score and 99% intraclass correlation coefficient). Conclusion: IRIS is feasible for fast, accurate liver cyst segmentation in patients with ADPKD.
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