Medical-Imaging | Maria Monzon

Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores

Mon, 01 Jun 2026 00:00:00 +0000

Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores

Mon, 01 Jun 2026 00:00:00 +0000

Carried out during a visiting research stay at the University of Oxford — Visual Geometry Group (Prof. Andrew Zisserman’s group). Keywords: Pairwise Ranking · Ordinal Regression · Spine Degeneration · MRI.

Beyond Fluency: A Clinical Benchmark and Anomaly-Enhanced Baseline for Spine MRI Report Generation

Mon, 01 Jun 2026 00:00:00 +0000

Segmentation Pre-Training for Efficient Spine Degeneration Grading

Mon, 01 Jun 2026 00:00:00 +0000

Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma

Mon, 18 May 2026 00:00:00 +0000

Quantifying central canal stenosis prediction uncertainty in SpineNet with conformal prediction

Tue, 20 Jan 2026 00:00:00 +0000

Uncertainty Quantification of Central Canal Stenosis Deep Learning Classifier From Lumbar Sagittal T2-Weighted MRI

Thu, 01 Jan 2026 00:00:00 +0000

Uncertainty-Aware Lumbar Stenosis Classifier

Thu, 01 Jan 2026 00:00:00 +0000

Journal project where I developed a deep learning classifier that grades central-canal stenosis severity from intervertebral volumes on lumbar sagittal MRI, and — crucially — quantifies how confident each prediction is.

Two complementary uncertainty-quantification strategies, Monte-Carlo dropout and test-time augmentation, turn the grading network into a stochastic predictor with mean and variance, so low-confidence cases can be flagged for clinical review rather than silently mis-graded.

Published in JOR Spine, 2026.

Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation

Tue, 01 Jul 2025 00:00:00 +0000

Lumbar Injection Satisfaction — Data-Driven Analysis

Tue, 01 Jul 2025 00:00:00 +0000

Data-driven project that retrospectively identifies which chronic low-back-pain (CLBP) patients benefit from lumbar steroid injections, using the clinical, demographic and patient-reported data of the TREXI study. The aim is to find key predictors of treatment satisfaction and to establish clinically meaningful pain-reduction thresholds.

Study design

212 participants completed questionnaires directly before (T0) and two weeks after (T1) the injection, covering pain intensity, patient-reported outcomes (COMI, PSEQ), and demographic and clinical variables.

Methodology

Missing values were imputed with Random Forest (numeric) and K-Nearest-Neighbours (categorical); features were standardised or encoded by type. Nested cross-validation trained Random Forest, Logistic Regression and Gradient Boosting classifiers, with the best model optimised through Bayesian hyperparameter tuning. SHAP values interpreted the predictions and ROC analysis derived the pain-reduction thresholds.

Key results

A Random Forest model reached 0.865 average precision in predicting treatment satisfaction. SHAP analysis identified pain self-efficacy — coping mechanisms and maintained daily-activity performance — as the strongest predictors. A 2.03-point absolute (or 30 % relative) drop on the pain scale was found to be clinically meaningful.

Published in Scientific Reports (Nature), 2025. Supported by the PHRT Strategic Focus Area of the ETH Domain.

SpineSegDiff — Diffusion Models for Spine MRI Segmentation

Sun, 01 Jun 2025 00:00:00 +0000

Diffusion-based framework for automated segmentation of lumbar spine MRI, developed at ETH Zürich’s Biomedical Data Science Lab. It produces multiclass segmentations of both T1- and T2-weighted scans — vertebral bodies, intervertebral discs and the spinal canal — with a focus on imaging in low-back-pain patients, trained and evaluated on the multi-centre SPIDER dataset.

Problem

Manual segmentation of spine MRI is time-consuming and operator-dependent, while reliable, modality-agnostic automated tools are needed to support diagnosis and management of degenerative spine conditions. SpineSegDiff targets multi-modal (T1w and T2w) lumbar spine segmentation and aims to provide not just accurate masks but also a measure of prediction reliability for clinical decision-making.

Method

The approach builds on the DiffUNet framework: a U-shaped denoising network with a dedicated multi-scale image encoder processes the MRI jointly with a partially-noised segmentation mask and learns to denoise toward the correct segmentation. The central contribution is a presegmentation training strategy — a pre-trained nnU-Net generates an initial mask which is then partially noised rather than starting from pure noise. This reduces the required diffusion timesteps from 1000 down to roughly 30 while maintaining accuracy, substantially improving inference efficiency. The diffusion sampling process also enables uncertainty quantification and ensemble prediction.

Key features

Multi-modal MRI segmentation across T1-weighted and T2-weighted scans.
Multiclass output covering lumbar spine anatomy (vertebral bodies, intervertebral discs, spinal canal).
Uncertainty quantification via diffusion sampling, producing heatmaps that highlight anatomical boundaries.
Ensemble prediction and configurable cross-validation folds.
Efficient inference using a small number of sampling timesteps (enabled by the presegmentation strategy).

Data & results

Trained and evaluated on the SPIDER dataset — a multi-centre lumbar spine MRI collection (Radboudumc, Jeroen Bosch Hospital, Rijnstate Hospital, Sint Maartenskliniek). SpineSegDiff achieves segmentation performance comparable to the state-of-the-art nnU-Net, with particular strength on degenerated intervertebral discs, while additionally producing uncertainty heatmaps that give clinicians a reliability signal.

Implementation

Implemented in Python with PyTorch and the MONAI medical-imaging framework; training and inference run via command-line scripts with configurable data directories, class counts, timesteps and folds. Published at MIDL 2025 (Monzón, Iff, Konukoglu, Jutzeler — Monzón & Iff co-first). Code released under the Apache 2.0 license, building on the SPIDER dataset, MONAI, and the DiffUNet architecture.

Automatic Determination of a Motion Parameter of the Heart

Tue, 04 Mar 2025 00:00:00 +0000

Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower Back Pain Patients

Tue, 10 Dec 2024 00:00:00 +0000

A Fully Automated Pipeline for Extracting Vertebral Compression Parameters from Clinical MRI Scans

Mon, 27 May 2024 00:00:00 +0000

Fully automated AI-based cardiac motion parameter extraction — application to mitral and tricuspid valves on long-axis cine MR images

Fri, 01 Sep 2023 00:00:00 +0000

Fully automatic extraction of mitral valve annulus motion parameters on long-axis CINE CMR using deep learning

Sat, 01 May 2021 00:00:00 +0000

Mitral Valve Annulus Motion Parameter Extraction

Fri, 01 Jan 2021 00:00:00 +0000

Diastolic dysfunction is an important cause of cardiac insuf- ficiency, defined as a malfunctioning filling of the heart during diastole. The analysis of mitral valve motion is known to be relevant in the diagno- sis of cardiac dysfunction. Cardiac motion parameters can be extracted from Cardiac Magnetic Resonance (CMR) images. However, in clinical setting valve motion modeling usually needs a manual intervention to lo- calize the valvular plane. We propose two chained Convolutional Neural Networks (CNN) for automatic tracking of mitral valve-annulus land- marks on time-resolved 2-chamber and 4-chamber CMR images. The first CNN is trained to detect the region of interest and the second to track the landmarks along the cardiac cycle. The presented deep learn- ing system has high accuracy in terms of temporal landmark tracking and motion assessment. Furthermore, we successfully extracted several motion-related parameters thereby overcoming time-consuming annota- tion and allowing statistical analysis over a large number of datasets.

Assessment of Cardiac Valve Motion on Time-Resolved MRI

Wed, 01 Jul 2020 00:00:00 +0000

The presented work aimed to develop a novel method for the task of valve motion assessment for prospective slice tracking in CMR temporal image (CINE) acquisition. The objective this thesis ¹ was to evaluate deep learning-based landmarks tracking methods to extract the motion of the mitral valves throughout the cardiac cycle on time-resolved CMR four-chamber- view (4CHV) images. The fully automated CNN based algorithm would improve the current slice following acquisition workflow, which needs from manual user intervention. The automatic imaging slice tracking will enable a more precise morphology and flow estimation, potentially improving the diagnosis of diastolic dysfunction. During this project, an automatic deep learning algorithm using landmark detection was developed.

Motivation

Cardio-vascular diseases remain the leading cause of death worldwide [1]. In particular, diastolic dys- function is an important cause of cardiac insufficiency, heart failure, potentially leading to premature cardiovascular death if not diagnosed and treated for at an early disease stage. Left ventricular dias- tolic dysfunction is estimated to affect from 27% to 43% in middle-aged adults [2] and its prevalence increases with age.

Diastolic dysfunction is defined as a malfunctioning filling of the heart during diastole [3]. Its diag- nostic remains challenging as not only structural but functional abnormalities need to be evaluated [4]. Several non-invasive imaging techniques have been used for assessing diastolic dysfunction. Typically, intra-cardiac haemodynamics are measured using Doppler echocardiography. The peak velocities during early diastolic filling (E wave) and atrial contraction (A wave) are measured, and their ratio is calculated [5]. The Doppler flow measures are influenced by multiple factors including age, valve heart diseases, heart rate. Therefore, no single parameter is determinant enough to asses diastolic dysfunction [6].

Magnetic Resonance Imaging (MRI) is a noninvasive technique, well suited for disease diagnosis and monitoring due to its high spatial and temporal resolution and excellent soft-tissue contrast. In- deed, Cardiac Magnetic Resonance (CMR) provides information about heart structure and function, particularly in soft tissue characterization without the need of any contrast agent. Furthermore, CMR is useful for characterizing the anatomic valve morphology and cine images allows to visualize the valve throughout the cardiac cycle [7, 8]. Indeed, CMR allows assessment of blood flow using phase-contrast MRI (PC-MRI) [9].

As the valves move during a cardiac cycle, the acquisition of a fixed 2D slice will not allow the accurate time-resolved visualization of the valve and quantification of the blood flow: The valve moves into and out of the MRI slice so cannot be seen on each cardiac phase, therefore, the quantified flow through the valve is not correct. Despite the importance of correct valve flow assessment, time resolved CMR is yet usually performed at fixed slice positions throughout the cardiac cycle Kozerke et al. [10] introduced the prospective slice tracking concept. If the valve motion is known prior to the examina- tion, the slice position can be updated for each cardiac phase. As this approach however requires a dedicated pre-scan including an image-based algorithm to quantify the valve motion, very few related work can be found applying this technique in a clinical setting.

The recent development of deep learning has led to significant improvements in medical image analysis [11]. The prominent algorithms for feature tracking include deep learning systems based on convolutional neural network architectures. Specifically, for valve-tracking, a deep learning feature tracking algorithm for automatic slice-tracking can be developed, overcoming many limitations of the current valve-tracking techniques. The imaging slice tracking will enable a more precise morphology and flow estimation, potentially improving the diagnosis of diastolic dysfunction.

Dataset

The dataset is composed of Cardiac Magnetic Resonance Imaging 4CHV CINE series from 87 patients extracted from the Cardiac Atlas Project [Fon11]. The imaging protocol included CINE images acquired in long-axis planes, but for our work we only selected the 4CHV long axis view. The sequence of 4CHV CINE CMR are acquired on multiple 1.5T MRI scanners from different vendors (Philips, Siemens, GE). The mean pixel spacing of all selected datasets is 1.49 + 0.28 mm/pixel. Additionally the annotated dataset contains anatomical landmarks, i.e. the mitral valve position at each temporal aframe nnotated by an experienced analyst. As a preprocessing step, the CINE series were interpolated into a fixed and temporal resolution and horizontally flipped to have all the images oriented with the apex of the heart upwards. Finally,the input images intensity were normalized to 0-1 range values. After cleaning the data, the dataset contain 87 series that are split into 70% for training and the rest 30% equally between test and validation. To increase the variability of the dataset, online data augmentation was performed when training the network in forms of shift, center cropping, rotation, Guassian noise addition, contrast enhancement and Gaussian blurring.

System Overview

The motion assesment system is composed of four main stages. The preprocessed CINE dataset are forwarded as input to a convolutional based neural network (CNN). The final proposed system comprises two chained CNNs based on heatmap regression approach: Localization Network + Landmark Detection Network. The task of the localization CNN model is to detect the landmarks only in the first temporal frame of the full 4CHV CINE series. The complete system is designed to regress the mitral valve-annulus landmarks for each time-frame points. Finally, the valvular plane motion can be derived from the two predicted landmark distance. The predicted temporal coordinates are translated into mm-space.

Results

Evaluated heatmap based regression approach for landmark detection yields to a superior performance than direct coordinate regression. Especially, the motion is better modeled by the two-stages architectures than with 3-D instead of single network. The proposed system showed an accurate match between expert-annotated and automatically detected landmarks for each time-frame. An example results of the model is shown in the below figure:

The complete system has an accuracy of 1.66+- 0.75 mm. The roughness metric has a similar value as the ground truth annotations R = 0.085 +- 0.045 and the total slope variation value is TVslope = 0.53 +- 0.28.

Our proposed method allows landmark localization with sub-pixel accuracy. Experiments show that our approach is able to correctly locate the landmark which can assess the prospective slice tracking acquisition method. The results of this work can be summarized as follows:

CNNs can be used to develop a time-resolved landmark tracking application without the need of any user interaction.
Heart landmark detection with the deep learning results can be improved by means of regressing heatmaps.
The proposed two-stages method further allows a more accurate localization.
A post-processing step with the non-linear least square fitting is able to refine the landmark location. The additional step outputs subpixel maxima and therefore predicts a smooth motion.

Conclusion and Outlook

The proposed system enables the valve tracking detection over time and therefore smooth valve motion assessment. Future work would focus on extension on the algorithm to 2CHV valve landmark detection. Evaluation with more test data and further refinement of the network. An integration of a single CNN could be convenient for faster inference time. Finally, when the validity of the method is proved , the scanner integration would be the next step.

References

[1] World Health Organization, World Health Statistics 2019: Monitoring Health for the SDGs. Geneva, Switzerland: World Health Organization, 2019.

[2] M. Nayor, L. L. Cooper, D. M. Enserro, V. Xanthakis, M. G. Larson, E. J. Benjamin, J. Aragam, G. F. Mitchell, and R. S. Vasan, “Left ventricular diastolic dysfunction in the community: Impact of diagnostic criteria on the burden, correlates, and prognosis,” Journal of the American Heart Association, vol. 7, no. 11, 2018.

[3] A. Kossaify and M. Nasr, “Diastolic dysfunction and the new recommendations for echocardiographic assessment of left ventricular diastolic function: Summary of guidelines and novelties in diagnosis and grading,” Journal of Diagnostic Medical Sonography, vol. 35, no. 4, pp. 317–325, 2019.

[4] C. Gutierrez and D. G. Blanchard, “Diastolic heart failure: Challenges of diagnosis and treatment,” American Family Physician, vol. 69, no. 11, pp. 2609–2616, 2004.

[5] C. Dugo, M. Rigolli, A. Rossi, and G. A. Whalley, “Assessment and impact of diastolic function by echocardiography in elderly patients.,” Journal of geriatric cardiology : JGC, vol. 13, no. 3, pp. 252–25260, 2016.

[6] S. F. Nagueh, O. A. Smiseth, C. P. Appleton, I. Byrd, Benjamin F., H. Dokainish, T. Edvardsen, F. A. Flachskampf, T. C. Gillebert, A. L. Klein, P. Lancellotti, P. Marino, J. K. Oh, B. Alexandru Popescu, and Waggoner, “Recom- mendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging,” European Heart Journal - Cardiovascular Imaging, vol. 17, pp. 1321–1360, 07 2016.

[7] S. Shah, E. D. Chryssos, and H. Parker, “Magnetic resonance imaging: a wealth of cardiovascular information.,” The Ochsner journal, vol. 9, no. 4, pp. 266–77, 2009.

[8] K. Maganti, V. H. Rigolin, M. E. Sarano, and R. O. Bonow, “Valvular heart disease: diagnosis and management.,” Mayo Clinic proceedings, vol. 85, pp. 483–500, may 2010.

[9] P. Waheed, A. K. Naveed, and F. Farooq, “Cardiovascular magnetic resonance physics for clinicians: part I,” Journal of the College of Physicians and Surgeons Pakistan, vol. 19, no. 4, pp. 207–210, 2009.

[10] S. Kozerke, J. Schwitter, E. M. Pedersen, and P. Boesiger, “Aortic and mitral regurgitation: Quantification using moving slice velocity mapping,” Journal of Magnetic Resonance Imaging, vol. 14, no. 2, pp. 106–112, 2001.

[11] A. Maier, C. Syben, T. Lasser, and C. Riess, “A gentle introduction to deep learning in medical image processing,” Zeitschrift für Medizinische Physik, vol. 29, no. 2, pp. 86 – 101, 2019.

[12] S. S. Yoon, E. Hoppe, M. Schmidt, C. Forman, P. Sharma, C. Tilmanns, A. Maier, and J. Wetzl, “Automatic Cardiac Resting Phase Detection for Static Cardiac Imaging Using Deep Neural Networks,” in Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition) (I. S. for Magnetic Resonance in Medicine, ed.), 2019.

All the algorithims will be developed in python using the Pytorch deep learning framework. The data for training, validation and testing will be provided by Siemens Healthineers. ↩︎

Myocardial Pathology Segmentation (Multi-Sequence CMR)

Wed, 01 Jul 2020 00:00:00 +0000

Introduction

Accurate segmentation of myocardial pathological tissue from cardiac magnetic resonance images (CMR), such as scar tissue and edema, f is crutial to the assessment of the severity of myocardial infarction (MI). CMRis the gold standar to provide anatomical and functional information of heart. Specifically, late gadolinium enhancement (LGE) CMR sequence which is used to diagnosis MI, the T2-weighted CMR which resembles ischemic regions, and the balanced- Steady State Free Precession (bSSFP) cine sequence which captures cardiac motions . Combining these multi-sequence CMR data could provide reliable information regarding to the pathological as well as morphological information of the myocardium

Data

The input dataset contains 45 cases of multi-sequence CMR from Myops2020 challenge . Each case refers to a patient with three sequence CMR, i.e., LGE, T2 and bSSFP CMR. All these clinical data have got institutional ethic approval and have been anonymized. The “masks” folder contains 45 cases of multi-sequence CMR, where each mask represents the segmentation map of the the corresponding CMR Slice image:

Label 1: normal myocardium
Label 2: edema
Label 3: scar
Label 0: Background

The proposed approach involves a two-staged network. First a heatmap-based regression architecture was train to detect a small ROI and locate the myocardium, based on , in order to reduce task complexity. Sequentially a a U-Net based neural network was train to perform multi-modal pathological region segmentation.

ECG Annotation & Arrhythmia Classifier

Wed, 27 Apr 2016 00:00:00 +0000

Out-of hospital cardiac arrest (OHCA) is one of the major causes of death in developed countries. Resuscitation guidelines recommend different treatments depending on the heart rhythm of the patient. The objective of this work is to develop a machine learning algorithm based on the ECG signal to automatically label heart rhythms in resuscitation episodes, a key tool for the retrospectively evaluation and improvement of the quality treatment. This work would help to systematise the annotation of databases since manual annotation of rhythms is a time-consuming task which can be an obstacle for handling large data sets.

The starting point of this project was a database composed of 1631 intervals of 3 seconds taken from a larger database containing OHCA 298 episodes. To review the ECG segments, a graphical interface (GUI) was developed which allows the display of the ECGs classified by the type of arrhythmia: asystole (AS), ventricular tachycardia (VT) that degenerates into ventricular fibrillation (VF), pulseless electrical activity (PEA) pulse generating rhythms (PR).

The database has been processed using a machine learning algorithm and the results obtained using cross-validation. Two classifiers have been developed selecting five features of the ECG, first to identify AS, and then to discriminate organised rhythms (PR and PEA) from ventricular arrhythmias (VT and VF).

These algorithms have been combined to create a three class rhythm classification algorithm. The total accuracy of the final algorithm was 90.9%. A precise algorithm was obtained for the classification of OHCA rhythm into: AS, organised, and shockable rhythms. This algorithm can be implemented to analyse resuscitation episodes using 3 seconds ECG segments, and could be integrated into new methods for retrospective analysis of OHCA.

The results show that it is possible to automatically interpret resuscitation cardiac rhythm. These types of algorithms can be very useful since they allow an efficient rhythm classification with a minimum level of expert clinician supervision.