Medical Image Computing and Computer-assisted Intervention : MICCAI ...

Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces generated by deep learning via self-organizing maps (SOM). SOM separates the latent space into clusters and then maps the cluster centers to a discrete (typically 2D) grid preserving the high-dimensional relationship between clusters. However, learning SOM in a high-dimensional latent space tends to be unstable, especially in a self-supervision setting...

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians...

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed , multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being...

Ultrasound (US)-probe motion estimation is a fundamental problem in automated standard plane locating during obstetric US diagnosis. Most recent existing recent works employ deep neural network (DNN) to regress the probe motion. However, these deep regressionbased methods leverage the DNN to overfit on the specific training data, which is naturally lack of generalization ability for the clinical application. In this paper, we are back to generalized US feature learning rather than deep parameter regression...

Eye trackers can provide visual guidance to sonographers during ultrasound (US) scanning. Such guidance is potentially valuable for less experienced operators to improve their scanning skills on how to manipulate the probe to achieve the desired plane. In this paper, a multimodal guidance approach (Multimodal-GuideNet) is proposed to capture the stepwise dependency between a real-world US video signal, synchronized gaze, and probe motion within a unified framework. To understand the causal relationship between gaze movement and probe motion, our model exploits multitask learning to jointly learn two related tasks: predicting gaze movements and probe signals that an experienced sonographer would perform in routine obstetric scanning...

Video quality assurance is an important topic in obstetric ultrasound imaging to ensure that captured videos are suitable for biometry and fetal health assessment. Previously, one successful objective approach to automated ultrasound image quality assurance has considered it as a supervised learning task of detecting anatomical structures defined by a clinical protocol. In this paper, we propose an alternative and purely data-driven approach that makes effective use of both spatial and temporal information and the model learns from high-quality videos without any anatomy-specific annotations...

The inter-site variability of diffusion magnetic resonance imaging (dMRI) hinders the aggregation of dMRI data from multiple centers. This necessitates dMRI harmonization for removing non-biological site-effects. Recently, the emergence of high-resolution dMRI data across various connectome imaging studies allows the large-scale analysis of cortical micro-structure. Existing harmonization methods, however, perform poorly in the harmonization of dMRI data in cortical areas because they rely on image registration methods to factor out anatomical variations, which have known difficulty in aligning cortical folding patterns...

Inter-frame patient motion introduces spatial misalignment and degrades parametric imaging in whole-body dynamic positron emission tomography (PET). Most current deep learning inter-frame motion correction works consider only the image registration problem, ignoring tracer kinetics. We propose an inter-frame Motion Correction framework with Patlak regularization (MCP-Net) to directly optimize the Patlak fitting error and further improve model performance. The MCP-Net contains three modules: a motion estimation module consisting of a multiple-frame 3-D U-Net with a convolutional long short-term memory layer combined at the bottleneck; an image warping module that performs spatial transformation; and an analytical Patlak module that estimates Patlak fitting with the motion-corrected frames and the individual input function...

Head movement is a major limitation in brain positron emission tomography (PET) imaging, which results in image artifacts and quantification errors. Head motion correction plays a critical role in quantitative image analysis and diagnosis of nervous system diseases. However, to date, there is no approach that can track head motion continuously without using an external device. Here, we develop a deep learning-based algorithm to predict rigid motion for brain PET by lever-aging existing dynamic PET scans with gold-standard motion measurements from external Polaris Vicra tracking...

Segmentation of the prostate into specific anatomical zones is important for radiological assessment of prostate cancer in magnetic resonance imaging (MRI). Of particular interest is segmenting the prostate into two regions of interest: the central gland (CG) and peripheral zone (PZ). In this paper, we propose to integrate an anatomical atlas of prostate zone shape into a deep learning semantic segmentation framework to segment the CG and PZ in T2-weighted MRI. Our approach incorporates anatomical information in the form of a probabilistic prostate zone atlas and utilizes a dynamically controlled hyperparameter to combine the atlas with the semantic segmentation result...

Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task. Current multi-modality registration techniques maximize hand-crafted inter-domain similarity functions, are limited in modeling nonlinear intensity-relationships and deformations, and may require significant re-engineering or underperform on new tasks, datasets, and domain pairs. This work presents ContraReg, an unsupervised contrastive representation learning approach to multi-modality deformable registration...

Contrastive learning (CL) aims to learn useful representation without relying on expert annotations in the context of medical image segmentation. Existing approaches mainly contrast a single positive vector ( i.e. , an augmentation of the same image) against a set of negatives within the entire remainder of the batch by simply mapping all input features into the same constant vector. Despite the impressive empirical performance, those methods have the following shortcomings: (1) it remains a formidable challenge to prevent the collapsing problems to trivial solutions; and (2) we argue that not all voxels within the same image are equally positive since there exist the dissimilar anatomical structures with the same image...

Spherical mapping of cortical surface meshes provides a more convenient and accurate space for cortical surface registration and analysis and thus has been widely adopted in neuroimaging field. Conventional approaches typically first inflate and project the original cortical surface mesh onto a sphere to generate an initial spherical mesh which contains large distortions. Then they iteratively reshape the spherical mesh to minimize the metric (distance), area or angle distortions. However, these methods suffer from two major issues: 1) the iterative optimization process is computationally expensive, making them not suitable for large-scale data processing; 2) when metric distortion cannot be further minimized, either area or angle distortion is minimized at the expense of the other, which is not flexible to generate application-specific meshes based on both of them...

Percutaneous nephrolithotomy (PCNL) is considered a first-choice minimally invasive procedure for treating kidney stones larger than 2 cm. It yields higher stone-free rates than other minimally invasive techniques and is employed when extracorporeal shock wave lithotripsy or uteroscopy are, for instance, infeasible. Using this technique, surgeons create a tract through which a scope is inserted for gaining access to the stones. Traditional PCNL tools, however, present limited maneuverability, may require multiple punctures and often lead to excessive torquing of the instruments which can damage the kidney parenchyma and thus increase the risk of hemorrhage...

In order to tackle the difficulty associated with the ill-posed nature of the image registration problem, regularization is often used to constrain the solution space. For most learning-based registration approaches, the regularization usually has a fixed weight and only constrains the spatial transformation. Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the "one value fits all" training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness...

Advanced contemporary diffusion models for tissue microstructure often require diffusion MRI (DMRI) data with sufficiently dense sampling in the diffusion wavevector space for reliable model fitting, which might not always be feasible in practice. A potential remedy to this problem is by using deep learning techniques to predict high-quality diffusion microstructural indices from sparsely sampled data. However, existing methods are either agnostic to the data geometry in the diffusion wavevector space ( <mml:math xmlns:mml="https://www...

Small bowel path tracking is a challenging problem considering its many folds and contact along its course. For the same reason, it is very costly to achieve the ground-truth (GT) path of the small bowel in 3D. In this work, we propose to train a deep reinforcement learning tracker using datasets with different types of annotations. Specifically, we utilize CT scans that have only GT small bowel segmentation as well as ones with the GT path. It is enabled by designing a unique environment that is compatible for both, including a reward definable even without the GT path...

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices...

Vision-and-language (V&L) models take image and text as input and learn to capture the associations between them. These models can potentially deal with the tasks that involve understanding medical images along with their associated text. However, applying V&L models in the medical domain is challenging due to the expensiveness of data annotations and the requirements of domain knowledge. In this paper, we identify that the visual representation in general V&L models is not suitable for processing medical data...

Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks have increased the feasibility of adopting SSM in medical practice by reducing the expert-driven manual and computational overhead in traditional SSM workflows. However, translating such frameworks to clinical practice requires calibrated uncertainty measures as neural networks can produce over-confident predictions that cannot be trusted in sensitive clinical decision-making...