Conference Agenda

Clinical biomarker monitoring of vulnerable newborn patients relies on invasive and costly laboratory procedures, increasing the risk for undetected metabolic disbalances and delaying medical assistance. Addressing this critical need, we present a non-invasive, wearable paper-based sensor patch that captures multiple critical body functions via colorimetric analysis of body fluids (sweat and saliva). The sensor facilitates simultaneous measurement of four health biomarkers: temperature (T), pH, sodium (S), and glucose (G), on a miniaturized surface integrating twelve different colorimetric inks.

The sensing wearable patch is supported by a video-recording setup and an artificial intelligence (AI) imaging system with two main goals: (1) to enable accurate digital color-based parameter reading, accounting for the sensor deformation and applying color correction to compensate for the uncontrolled light conditions of the clinic, and (2) perform sensor tracking and segmentation on a simulated moving patient in the neonatal incubator (Fig. 1).

The sensor in-vitro colorimetric dye responses showed high precision and reproducibility in sensing ranges relevant for neonatal care (T: 32-41 [°C]; pH: 3-9; S: 2.92-29.20 [mg/mL], G: 0.039-0.625 [mg/mL]). We propose a U-Net denoising autoencoder and latent feature regression, for quantification of the colorimetric response and simultaneous color correction. The model achieved high precision for automated parameter measurement (T:0.455 [°C]; pH:0.416; S:0.857 [mg/mL], G:0.019 [mg/mL]; mean absolute error) (Fig. 2). High performance was also found for tracking and segmentation of the sensing patch (Mask-RCNN) in a testbench with baby dummies in a neonatal incubator (0.986 AP @ IoU=0.5) (Fig. 3).

The sensor’s optimized interface for biofluid sample collection and multi-biomarker measurement on skin, together with the high AI imaging system performance under clinically relevant conditions, demonstrate the feasibility of an AI-enabled colorimetric sensor that caters to the critical needs of non-invasive newborn health monitoring in the clinic. This novel medical technology offers a low-cost, easily applicable, miniaturized solution, delivering critical information at the point of care, thereby aligning with the emerging landscape of digital health technologies while expanding access to high-quality care in the neonate.

Multi-contrast MRI sequences acquire multiple images with different tissue contrasts in a single scan, offering rich insight into tissue microstructure. However, their clinical use is limited by long acquisition times. A common strategy to reduce scan duration is k-space undersampling, though this makes image reconstruction more challenging. Advanced reconstruction approaches, including compressed sensing and deep learning, have shown promise in mitigating these issues.

In this work, we leverage shared anatomical information across contrasts to further increase acceleration. We use complementary variable density Poisson disk sampling [1] that fully samples the k-space center of each contrast for reliable contrast encoding while applying complementary undersampling patterns across contrasts for the high-frequency components. To reconstruct the resulting highly undersampled data, we propose an implicit neural representation (INR) network that jointly reconstructs all contrast images, effectively exploiting the complementarity of the acquired data [2]. Our INR-based method is self-supervised and trained individually for each scan, eliminating the need for external training data, an important advantage in multi-contrast MRI, where large training datasets are often unavailable.

We demonstrate the effectiveness of our approach on multi-contrast MRI data acquired with a cartesian 3D MPnRAGE sequence [3] and show that our INR-based reconstruction outperforms both traditional and deep learning–based reconstruction techniques. With our approach, the scan time at 1.5 mm resolution can be reduced from originally 22 minutes down to only 3.25 minutes.

Our code is available at: https://github.com/compai-lab/2025-miccai-niessen

References

1. 3D Cartesian MRI with compressed sensing and variable view sharing using complementary poisson-disc sampling. Levine E, Daniel B, Vasanawala S, Hargreaves B, Saranathan M. 2017 May. pp. 77(5):1774-1785.
2. INR Meets Multi-contrast MRI Reconstruction. Niessen N, Pirkl CM, Solana AB, Eichhorn H, Spieker V, Huang W, Sprenger T, Menzel MI, Schnabel JA. s.l. : RIME and GRAIL workshops MICCAI 2025, pp. 23–33, Springer-Verlag, Berlin, Heidelberg, 2025, 2025.
3. Highly accelerated 3D MPnRAGE meets implicit neural representation (INR) reconstruction. Niessen, N., Solana, A. B., Sprenger, T., Pirkl, C. M., Eichhorn, H., Schulte, R. F., Menzel, M. I., and Schnabel, J. A. on behalf of the PREDICTOM consortium. s.l. : ISMRM Medicine conference proceedings, 2026.

Diffusion Spectrum Imaging (DSI) samples the MRI signal in three-dimensional q-space and enables the reconstruction of diffusion distributions in biological tissue. Dense sampling of q-space requires a large number of measurements and results in long acquisition times. Methods for accelerating DSI acquisition have therefore been proposed. Reducing the number of required measurements while preserving the diffusion signal is therefore a central objective in the acceleration of DSI. Recent work has explored machine learning approaches for DSI reconstruction from incomplete measurements. Since the DSI signal is complex-valued and contains both magnitude and phase information, reconstruction methods must account for the complex signal representation.

A complex-valued residual encoder–decoder architecture is used to reconstruct fully sampled q-space volumes from undersampled measurements. The network processes volumetric DSI data using complex-valued 3D convolutions and residual blocks. Training is based on a weighted combination of loss terms defined on complementary properties of the complex signal, including amplitude error, wrapped phase error, logarithmic magnitude error, global energy consistency, and total variation regularization.

Experiments were conducted on 10 000 synthetic diffusion datasets. The simulated signals follow the methodological approach of previous work and are based on multi-tensor diffusion models originally introduced in earlier studies. The underlying diffusion process is modeled using Brownian motion within a voxel according to the diffusion MRI signal model. Fully sampled q-space volumes were used as reference data, while degraded inputs were generated through controlled undersampling and by adding complex Gaussian noise with noise levels randomly sampled from predefined ranges (0.2–0.4 for low noise and 0.7–0.9 for high noise). Training was performed using the Adam optimizer.

Reconstruction remains stable down to a keep fraction of approximately 25%. For a keep fraction of 75%, the structural similarity index (SSIM) is greater than or equal to 0.98 while the global error remains below 5%. When the keep fraction is reduced to 50%, the SSIM remains greater than or equal to 0.96 and the global error stays below 8%. At a keep fraction of 25%, the SSIM is still greater than or equal to 0.95 with a global error below 10%. Even at a very low keep fraction of 10%, the SSIM remains around 0.88 while the global error is still below 10%.

Hematological diagnosis requires assessing cell population composition, such as blast-to-mature ratios or co-occurring dysplastic lineages. Current Multiple Instance Learning (MIL) pipelines discard these relationships: mean pooling treats cells as independent, and Attention-Based MIL (ABMIL) learns weights without modeling interactions. Graph Neural Networks (GNNs) can capture such structure, but cytology lacks a predefined graph topology; the graph must be inferred from learned features.

We present HematoGraph, a framework that jointly learns a latent cell graph for slide-level classification. Our main contribution is the first application of Continuous Differentiable Graph Models (cDGM) to hematology: cDGM parameterizes a soft adjacency with learned temperature and threshold, yielding a fully differentiable graph optimized end-to-end. We pair cDGM with Adaptive Convolutions on Graphs (ACM) message passing and graph-aware Differentiable Pooling (DiffPool) aggregation, where a Graph Convolutional Network (GCN) based assignment groups neighboring cells into clusters before pooling. We benchmark against heuristic k-Nearest Neighbors (k-NN), discrete DGM with Gumbel top-k sampling, Snowflake variants, and Poincaré ball embeddings, combined with GCN, Graph Attention Networks (GAT), and five aggregators: mean, ABMIL, DiffPool, virtual node, and Graph Multiset Transformer (GMT). We ablate components on a 4-class subtyping task—Acute Myeloid Leukemia (AML), Myelodysplastic Syndromes (MDS), Myeloproliferative Neoplasms (MPN), and Normal—and evaluate the configuration across 8 tasks. Ablations validate each choice: cDGM yields the best topology (86.0% vs. 82.4% without graph), ACM outperforms GCN (83.6%) and GAT (78.8%), and DiffPool (86.0%) surpasses ABMIL (84.9%) and GMT (81.3%). Across all tasks, cDGM+ACM+DiffPool significantly outperforms mean pooling (+3.5 pp, p=0.025, winning 7/8 tasks) and ABMIL on 6/8 tasks (+1.5 pp), with gains up to +20.6 pp on AML/Acute Lymphoblastic Leukemia (ALL) and +5.9 pp on Dresden morphology. On AML Hehr, ABMIL retains a +5.7 pp edge, suggesting attention suits tasks where signals lie in rare cells rather than communities. Graph-aware hierarchical pooling via cDGM+ACM+DiffPool provides a principled alternative to standard MIL for cytology. DiffPool is the only aggregator systematically benefiting from graph structure (p<0.05 for 7/9 graph learners); ABMIL and GMT degrade when graphs are introduced, indicating structure helps only when the aggregator can exploit topology

Instance segmentation in crowded scenes requires both high object capacity and precise boundary modeling. Recent promptable foundation models offer strong generalization, but their automatic mask generation can be unreliable outside the training domain and their computational cost scales unfavorably with very large numbers of instances. We present CPNv2, a modernized contour-based instance segmentation framework that combines dense proposal prediction with learned, sub-pixel accurate refinement. CPNv2 introduces centroid-based Hungarian target assignment to reduce ambiguous supervision on low-resolution output grids, iterative score refinement for stable confidence estimation, differentiable feature sampling via bilinear interpolation for continuous contour updates, and deep contour refinement with a contour refinement transformer trained with contour denoising. Across our experiments, CPNv2 consistently improves over the previously established CPN architecture and achieves strong results across diverse scientific imaging datasets in terms of F1@50 and PQ@50 scores. At the same time, it preserves a key strength of contour-based segmentation: among the evaluated methods in our experiments, contour-based models show the highest efficiency in terms of latency, memory, and output size. This makes CPNv2 particularly attractive for high-throughput settings, where large images with many instances must be processed efficiently without sacrificing segmentation quality. When benchmarking recent SAM variants we find that even under ground-truth prompting, they do not consistently match the strongest task-specific models on our datasets. Code and trained models will be made publicly available.