Adversarial Deformation Regularization for Training Image Registration Neural Networks

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.Comment: Accepted to MICCAI 201

Label-driven weakly-supervised learning for multimodal deformable image registration

Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201

La maladie de Hodgkin variant du syndrome de Richter (étude anatomo-clinique à propos de dix nouvelles observations issues de la série française du GOELAMS)

La survenue d'un lymphome agressif chez un patient atteint d'une leucémie lymphoïde chronique, pathologie dénommée syndrome de Richter, peut occasionnellement se traduire par l'apparition d'un lymphome de Hodgkin (MHRS). Il s'agit d'une lésion rare, seules quelques dizaines de cas ayant été décrits dans la littérature. Nous rapportons ici dix nouvelles observations issues de la série française du GOELAMS. Cette étude comporte une approche anatomo-clinique étendue, la recherche du virus d'Epstein-Barr et l'expression du ZAP-70.Les caractéristiques morphologiques et immunophénotypiques de la MHRS sont identiques à celles du type à " cellularité mixte " de la MH dans la population générale, associant un immunophénotype CD30+, CD15+, CD45- et l'absence habituelle d'antigènes B (CD20, CD79a). Elle en diffère cependant par une présentation agressive, disséminée d'emblée, la fréquence des signes généraux et par la présence constante du virus d'Epstein-Barr. Il s'agit donc d'une entité proche de celle du sujet immunodéprimé. La majorité des patients étaient ZAP-70+, ceci traduisant une LLC de très mauvais pronostic.NANCY1-SCD Medecine (545472101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Weakly-supervised convolutional neural networks for multimodal image registration

One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.Comment: Accepted manuscript in Medical Image Analysi

Serum albumin or body mass index: Which prognostic factor for survival in patients with acute myeloblastic leukaemia?

International audienc

Weakly-supervised convolutional neural networks for multimodal image registration

One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.status: publishe

Technical Note: Error metrics for estimating the accuracy of needle/instrument placement during transperineal magnetic resonance/ultrasound-guided prostate interventions

Purpose Image‐guided systems that fuse magnetic resonance imaging (MRI) with three‐dimensional (3D) ultrasound (US) images for performing targeted prostate needle biopsy and minimally invasive treatments for prostate cancer are of increasing clinical interest. To date, a wide range of different accuracy estimation procedures and error metrics have been reported, which makes comparing the performance of different systems difficult. Methods A set of nine measures are presented to assess the accuracy of MRI‐US image registration, needle positioning, needle guidance, and overall system error, with the aim of providing a methodology for estimating the accuracy of instrument placement using a MR/US‐guided transperineal approach. Results Using the SmartTarget fusion system, an MRI‐US image alignment error was determined to be 2.0 ± 1.0 mm (mean ± SD), and an overall system instrument targeting error of 3.0 ± 1.2 mm. Three needle deployments for each target phantom lesion was found to result in a 100% lesion hit rate and a median predicted cancer core length of 5.2 mm. Conclusions The application of a comprehensive, unbiased validation assessment for MR/US guided systems can provide useful information on system performance for quality assurance and system comparison. Furthermore, such an analysis can be helpful in identifying relationships between these errors, providing insight into the technical behavior of these systems

Sperm quality before treatment in patients with early stage Hodgkin’s lymphoma enrolled in EORTC-GELA Lymphoma Group trials

Although widely recommended, cryopreservation of sperm is sometimes not performed for patients with Hodgkin’s lymphoma because of presumed poor sperm quality related to the disease. In this large study of males with Hodgkin’s lymphoma, 90% had good or intermediate sperm quality, indicating that in most patients with early-stage Hodgkin’s lymphoma sperm quality before treatment is good enough for future fatherhood

CPX-351 Induces Deep Response and Suppress the Impact of Poor Prognosis Mutations (TP53, ASXL1, RUNX1 and EVI1) Defined By ELN-2017 in t-AML and MRC AML: A Report from a Multicentric French Cohort

61st Annual Meeting and Exposition of the American-Society-of-Hematology (ASH), Orlando, FL, DEC 07-10, 2019International audienceIntroductionCPX-351 is a liposomal formulation of cytarabine and daunorubicin packaged at a 5:1 molar ratio. This drug has recently been approved by FDA and EMEA for patients with therapy-related acute myeloid leukemia (t-AML) or AML with myelodysplasia-related changes (MRC-AML).The primary objective of this study was to analyze the efficacy of CPX-351 in a real-life setting, evaluating the impact of mutations on response and minimal residual disease (MRD) in responding patients.MethodsWe retrospectively collected data from patients treated by CPX-351 in eleven centers in France. Clinical, biological and treatment information were available for all patients. NGS (19 genes or more) was performed in 67 patients (84%) at diagnosis.Overall response rate (ORR) was defined by complete remission (CR) and CR with incomplete haematological recovery (CRi). Among the patients in CR or CRi, 25 (56%) had MRD evaluation assessed by NGS or flow cytometry. Overall survival (OS) was calculated from the date of AML diagnosis to the date of death or last follow-up. All statistical analyses were performed using SPSS v.22 software (IBM SPSS Statistics).ResultsBetween April 2018 and July 2019, 80 patients treated with CPX-351 were included in this study. Sex ratio M/F was 43/37 and median age was 66 years old (range 20-83). AML subtypes were MRC-AML (61%) including AML with prior myelodysplastic syndrome (MDS-AML) (33%), prior chronic myelomonocytic leukemia (CMML-AML) (7%), or t-AML (29%). Sixteen patients (20%) had received prior treatment by hypomethylating agents (HMA), at the time of MDS diagnosis, before AML evolution. According to ELN 2017 classification, genetic risk was favorable, intermediate and adverse in 1 (1%), 31 (38%) and 47 (58%), respectively. 36% and 28% patients had complex and monosomal karyotypes, respectively. Assessed by NGS the most frequent mutated gene were : RUNX1 (n=17, 25%), TP53 (n=15, 22%), ASXL1 (n=14, 21%), TET2 (n=13, 19%), DNMT3A (n=11, 16%), srsf2 (n=9, 13%), FLT3-ITD (n=8, 12%), CBL (n=7, 10%), WT1 (n=7, 10%), and EZH2 (n=7, 10%). According to a genetic ontogeny-based classifier (Lindsley et al., Blood 2015), 23 patients (34%), 29 (43%), 15 (22%) had de novo/pan-AML, secondary type mutations AML, and TP53 mutated AML, respectively.Only 4 patients discontinuing treatment due to prolonged haematological toxicity. Early death rate was 5% and 8.75% through day 30 and day 60, respectively.Median time to neutrophil recovery (>0,5 G/L) and platelet recovery (>20G/L) after induction was 29 days (range 19-78) and 28 days (range 12-77), respectively. Seventy-five patients (95%) had at least one grade 3 or more AEs, including 69 (86%) febrile neutropenia. We observed gastrointestinal toxicity 32 patients (40%) (nausea/vomiting (30%/11%), mucositis (15%)) including 4% with grade 3 or more and alopecia in only 12%.ORR was 45/80 (56%) after induction 1 including 53% CR and 3% CRi. ORR increased to 58% after induction 2. Among the 45 CR/CRi patients, 25 were evaluable for MRD at the time of the 1st consolidation. 72% had MRD below 10-3 (64% below 10-4). Prior treatment by HMA and presence of monosomal karyotype were identified as factors predicting a lower rate of CR/CRi (P=0.001 and P=0.002, respectively). Lindsley's classifier predicted significantly a better chemosensitivity in de novo/pan-AML mutations (P= 0.037). Poor molecular prognosis subgroups defined by 2017 ELN risk stratification (n = 53) as TP53, ASXL1, RUNX1 and EVI1 mutations were not associated with a lower response rate with CPX-351 (Table 1).Twenty-one (26%) patients underwent an allogeneic haematopoietic stem cell transplant (HSCT) with an improved median OS compared to non-transplanted patients (non reached vs 8 months, P= 0.004). With a median follow up of 8.5 months, median OS was not reached. Survival analysis in subgroups will be available for the ASH meeting.ConclusionThese data confirmed the efficacy and safety of CPX-351 in poor risk AML (t-AML and MRC-AML). The high rate of CR with low MRD compares favorably with previous report using 7+3 in elderly unfavorable AML (Sylvie D. Freeman et al., JCO 2013) and may explain the favorable outcome observed in patients after HSCT. Moreover, CPX-351 erases the poor prognosis associated with unfavorable mutations defined in 2017 ELN risk stratification. Lindsley's classifier was the best prognostic scoring system in patients treated by CPX-351