Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation

Any novel medical imaging modality that differs from previous protocols e.g. in the number of imaging channels, introduces a new domain that is heterogeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel space and introduce two new loss functions that promote semantic consistency. Firstly, we introduce a semantic cycle-consistency loss in the source domain to ensure that the translation preserves the semantics. Secondly, we introduce a pseudo-labelling loss, where we translate target data to source, label them by a source-domain network, and use the generated pseudo-labels to supervise the target-domain network. Our results show that this allows us to extract systematically better representations for the target domain. In particular, we address the challenge of enhancing performance on VERDICT-MRI, an advanced diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When compared to several unsupervised domain adaptation approaches, our approach yields substantial improvements, that consistently carry over to the semi-supervised and supervised learning settings

Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation

The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures

Synthesizing VERDICT maps from standard DWI data using GANs

VERDICT maps have shown promising results in clinical settings discriminating normal from malignant tissue and identifying specific Gleason grades non-invasively. However, the quantitative estimation of VERDICT maps requires a specific diffusion-weighed imaging (DWI) acquisition. In this study we investigate the feasibility of synthesizing VERDICT maps from standard DWI data from multi-parametric (mp)-MRI by employing conditional generative adversarial networks (GANs). We use data from 67 patients who underwent both standard DWI-MRI and VERDICT MRI and rely on correlation analysis and mean squared error to quantitatively evaluate the quality of the synthetic VERDICT maps. Quantitative results show that the mean values of tumour areas in the synthetic and the real VERDICT maps were strongly correlated while qualitative results indicate that our method can generate realistic VERDICT maps that could supplement mp-MRI assessment for better diagnosis

Identification of transcription-factor genes expressed in the Arabidopsis female gametophyte

Dongfang Wang, Changqing Zhang, David J. Hearn, Il-HO Kang, megan I. Skaggs, Karen S. Schumaker, and Ramin Yadegari are with the School of Plant Sciences, University of Arizona, Tucson, Arizona 85721-0036, USA -- Il-Ho Kang, Jayson A. Punwani, and Gary N. Drews are with the Department of Biology, University of Utah, Salt Lake City, Utah 84112-0840, USA -- Changqing Zhang is with The Section of Molecular, Cell and Developmental Biology, University of Texas at Austin, Austin, Texas 78712-0159, USA -- David J. Hearn is with the Department of Biological Sciences, Towson University, Towson, Maryland 21252-0001, USA -- Il-Ho Kang is with the Department of Horticulture, Iowa State University, Ames, Iowa 50011-1100, USA --Jayson A. Punwani is with the Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3280, USABackground In flowering plants, the female gametophyte is typically a seven-celled structure with four cell types: the egg cell, the central cell, the synergid cells, and the antipodal cells. These cells perform essential functions required for double fertilization and early seed development. Differentiation of these distinct cell types likely involves coordinated changes in gene expression regulated by transcription factors. Therefore, understanding female gametophyte cell differentiation and function will require dissection of the gene regulatory networks operating in each of the cell types. These efforts have been hampered because few transcription factor genes expressed in the female gametophyte have been identified. To identify such genes, we undertook a large-scale differential expression screen followed by promoter-fusion analysis to detect transcription-factor genes transcribed in the Arabidopsis female gametophyte. Results Using quantitative reverse-transcriptase PCR, we analyzed 1,482 Arabidopsis transcription-factor genes and identified 26 genes exhibiting reduced mRNA levels in determinate infertile 1 mutant ovaries, which lack female gametophytes, relative to ovaries containing female gametophytes. Spatial patterns of gene transcription within the mature female gametophyte were identified for 17 transcription-factor genes using promoter-fusion analysis. Of these, ten genes were predominantly expressed in a single cell type of the female gametophyte including the egg cell, central cell and the antipodal cells whereas the remaining seven genes were expressed in two or more cell types. After fertilization, 12 genes were transcriptionally active in the developing embryo and/or endosperm. Conclusions We have shown that our quantitative reverse-transcriptase PCR differential-expression screen is sufficiently sensitive to detect transcription-factor genes transcribed in the female gametophyte. Most of the genes identified in this study have not been reported previously as being expressed in the female gametophyte. Therefore, they might represent novel regulators and provide entry points for reverse genetic and molecular approaches to uncover the gene regulatory networks underlying female gametophyte development.Cellular and Molecular [email protected]

Non-small-cell lung cancer resectability: diagnostic value of PET/MR.

Purpose To assess the diagnostic performance of PET/MR in patients with non-small-cell lung cancer. Methods Fifty consecutive consenting patients who underwent routine 18F-FDG PET/CT for potentially radically treatable lung cancer following a staging CT scan were recruited for PET/MR imaging on the same day. Two experienced readers, unaware of the results with the other modalities, interpreted the PET/MR images independently. Discordances were resolved in consensus. PET/MR TNM staging was compared to surgical staging from thoracotomy as the reference standard in 33 patients. In the remaining 17 nonsurgical patients, TNM was determined based on histology from biopsy, imaging results (CT and PET/CT) and follow-up. ROC curve analysis was used to assess accuracy, sensitivity and specificity of the PET/MR in assessing the surgical resectability of primary tumour. The kappa statistic was used to assess interobserver agreement in the PET/MR TNM staging. Two different readers, without knowledge of the PET/MR findings, subsequently separately reviewed the PET/CT images for TNM staging. The generalized kappa statistic was used to determine intermodality agreement between PET/CT and PET/MR for TNM staging. Results ROC curve analysis showed that PET/MR had a specificity of 92.3 % and a sensitivity of 97.3 % in the determination of resectability with an AUC of 0.95. Interobserver agreement in PET/MR reading ranged from substantial to perfect between the two readers (Cohen’s kappa 0.646 – 1) for T stage, N stage and M stage. Intermodality agreement between PET/CT and PET/MR ranged from substantial to almost perfect for T stage, N stage and M stage (Cohen’s kappa 0.627 – 0.823). Conclusion In lung cancer patients PET/MR appears to be a robust technique for preoperative staging

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition

Purpose: VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC. // Materials and Methods: Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016–October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size. // Results: The mean lesion ADC for b0 b1500 had the lowest wSD in most zones (0.18–0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90–10.24x10-4 mm2/s). // Conclusion: ADC maps generated from b0 b1500 have better repeatability in normal TZ, normal PZ, and index lesions

The SmartTarget BIOPSY trial: A prospective, within-person randomised, blinded trial comparing the accuracy of visual-registration and MRI/ultrasound image-fusion targeted biopsies for prostate cancer risk stratification

Background: Multiparametric magnetic resonance imaging (mpMRI)-targeted prostate biopsies can improve detection of clinically significant prostate cancer and decrease the overdetection of insignificant cancers. Whether visual-registration targeting is sufficient or if augmentation with image-fusion software is needed is unknown. Objective: To assess concordance between the two methods. Design, Setting, and Participants: We conducted a blinded, within-person randomised, paired validating clinical trial. From 2014 to 2016, 141 men who had undergone a prior (positive or negative) transrectal ultrasound biopsy and had a discrete lesion on mpMRI (score 3 to 5) requiring targeted transperineal biopsy were enrolled at a UK academic hospital; 129 underwent both biopsy strategies and completed the study. Intervention: The order of performing biopsies using visual-registration and a computer-assisted MRI/ultrasound image-fusion system (SmartTarget) on each patient was randomised. The equipment was reset between biopsy strategies to mitigate incorporation bias. Outcome Measurements and Statistical Analysis: The proportion of clinically significant prostate cancer (primary outcome: Gleason pattern ≥3+4=7, maximum cancer core length ≥4 mm; secondary outcome: Gleason pattern ≥4+3=7, maximum cancer core length ≥6 mm) detected by each method was compared using McNemar's test of paired proportions. Results and Limitations: The two strategies combined detected 93 clinically significant prostate cancers (72% of the cohort). Each strategy individually detected 80/93 (86%) of these cancers; each strategy detected 13 cases missed by the other. Three patients experienced adverse events related to biopsy (urinary retention, urinary tract infection, nausea and vomiting). No difference in urinary symptoms, erectile function, or quality of life between baseline and follow-up (median 10.5 weeks) was observed. The key limitation was lack of parallel-group randomisation and limit on number of targeted cores. Conclusions: Visual-registration and image-fusion targeting strategies combined had the highest detection rate for clinically significant cancers. Targeted prostate biopsy should be performed using both strategies together. Patient Summary: We compared two prostate cancer biopsy strategies: visual-registration and image-fusion. The combination of the two strategies found the most clinically important cancers and should be used together whenever targeted biopsy is being performed

Comparison of PET/MRI With PET/CT in the Evaluation of Disease Status in Lymphoma

PURPOSE: The primary aim was to compare the diagnostic performance of PET/MRI (performed with basic anatomical MRI sequences) in detecting sites of disease in adult patients with lymphoma compared with the current standard of care, PET/CT. Secondary aims were to assess the additional value of diffusion-weighted imaging to PET/MRI in disease detection and to evaluate the relationship between the standardized uptake value on PET/MR and the apparent diffusion coefficient on diffusion-weighted imaging. METHODS: Sixty-eight studies in 66 consecutive patients with histologically proven Hodgkin or non-Hodgkin lymphoma were prospectively evaluated. Each patient had whole body PET/CT, followed by whole body PET/MR. Two experienced readers independently evaluated the PET/MRI studies, and two other experienced readers independently evaluated PET/CT. Site of lymphoma involvement and SUVmax at all nodal sites more avid than background liver were recorded. Readers provided stage (in baseline cases) and disease status (remission vs active disease). The apparent diffusion coefficient mean value corresponding to the most avid PET site of disease was recorded. RESULTS: Ninety-five nodal and 8 extranodal sites were identified on both PET/CT and PET/MRI. In addition, 3 nodal and 1 extranodal sites were identified on PET/MRI. For positive lesion detection, reader agreement in PET/MR was perfect between the 2 readers and almost perfect between PET/CT and PET/MR (k > 0.978). Intermodality agreement between PET/CT and PET/MRI was also near perfect to perfect for staging/disease status k = (0.979–1.000). SUVmax from PET/CT and PET/MRI correlated significantly (Spearman rho correlation coefficient, 0.842; P < 0.001). Diffusion-weighted imaging did not alter lesion detection or staging in any case. A negative correlation was demonstrated between ADC mean and SUVmax (Spearman rho correlation coefficient r, -0.642; P < 0.001). CONCLUSIONS: PET/MRI is a reliable alternative to PET/CT in the evaluation of patients with lymphoma. Diffusion-weighted imaging did not alter diagnostic accuracy. With comparable accuracy in detection of disease sites and added benefit of radiation dose reduction, PET/MRI has a potential to become part of routine lymphoma imaging

Development and evaluation of machine learning in whole-body magnetic resonance imaging for detecting metastases in patients with lung or colon cancer: a diagnostic test accuracy study.

OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker (P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support