Abstract

Accurate multi-organ segmentation in abdominal CT is essential for diagnosis, treatment planning, and disease monitoring. However, it suffers from inaccurate segmentation due to intricate spatial relationships and varying organ shapes. Existing deep learning methods often struggle with imbalanced classes, size bias, and ambiguous boundaries, hindering segmentation accuracy. To address these challenges, this paper proposes Guided-nnUNet, a two-stage segmentation framework that decomposes abdominal multi-organ segmentation into organ localization and then localization-guided fine segmentation. In the first stage, a ResNet-50 model generates a low-dimensional localization map guiding organ locations. This spatial guidance is then fed into the second stage, where a 3D U-Net with dynamic affine feature-map transform performs the fine-grained segmentation by integrating spatial context from the localization map. Our evaluation on the publicly available AMOS and BTCV datasets demonstrates the effectiveness of the model. Guided-nnUNet achieves an average improvement of 7% and 9% on the AMOS and BTCV datasets, respectively, compared to the baseline nnUNet model. Additionally, our model outperforms the state-of-the-art MedNeXt by 3.6% and 5.3% on the AMOS and BTCV datasets, respectively. These results suggest that our two-stage solution offers a promising approach for accurate abdominal organ segmentation, particularly for overcoming the challenges associated with complex organ structures.

Abstract

We present a novel solution for accurate segmentation of pneumothorax from chest radiographs utilizing free-text radiology reports. Our solution employs text-guided attention to leverage the findings in the report to initially produce a low-dimensional region-localization map. These prior region maps are integrated at multiple scales in an encoder-decoder segmentation framework via dynamic affine feature map transform (DAFT). Extensive experiments on a public dataset CANDID-PTX, show that the integration of free-text reports significantly reduces the false positive predictions, while the DAFT-based fusion of localization maps improves the positive cases. Our method achieves a DSC of 0.60 for positive and 0.052 FPR for positive and negative cases, respectively, and 0.70 to 0.85 DSC for medium and large pneumothoraces.

Abstract

No Results Found

Abstract

We present a modular and multi-level framework for the differential diagnosis of malignant melanoma. Our framework integrates contextual information and evidence at the lesion, patient, and population levels, enabling decision-making at each level. We introduce an anatomic-site aware masked transformer, which effectively models the patient context by considering all lesions in a patient, which can be variable in count, and their site of incidence. Additionally, we incorporate patient metadata via learnable demographics embeddings to capture population statistics. Through extensive experiments, we explore the influence of specific information on the decision-making process and examine the tradeoff in metrics when considering different types of information. Validation results using the SIIM-ISIC 2020 dataset indicate including the lesion context with location and metadata improves specificity by 17.15% and 7.14%, respectively, while enhancing balanced accuracy. The code is available at https://github.com/narenakash/meldd. Keywords: Melanoma Diagnosis · Differential Recognition · Ugly Duckling Context · Patient Demographics · Evidence-Based Medicine.

Abstract

Peripheral nerve blocks are crucial to treatment of post-surgical pain and are associated with reduction in perioperative opioid use and hospital stay. Accurate interpretation of sono-anatomy is critical for the success of ultrasound (US) guided peripheral nerve blocks and can be challenging to the new operators. This prospective study enrolled 227 subjects who were systematically scanned for supraclavicular and interscalene brachial plexus in various settings using three different US machines to create a dataset of 227 unique videos. In total, 41,000 video frames were annotated by experienced anaesthesiologists using partial automation with object tracking and active contour algorithms. Four baseline neural network models were trained on the dataset and their performance was evaluated for object detection and segmentation tasks. Generalizability of the best suited model was then tested on the datasets constructed from separate US scanners with and without fine-tuning. The results demonstrate that

Abstract

Peripheral nerve blocks are crucial to treatment of post-surgical pain and are associated with reduction in perioperative opioid use and hospital stay. Accurate interpretation of sono-anatomy is critical for the success of ultrasound (US) guided peripheral nerve blocks and can be challenging to the new operators. This prospective study enrolled 227 subjects who were systematically scanned for supraclavicular and interscalene brachial plexus in various settings using three different US machines to create a dataset of 227 unique videos. In total, 41,000 video frames were annotated by experienced anaesthesiologists using partial automation with object tracking and active contour algorithms. Four baseline neural network models were trained on the dataset and their performance was evaluated for object detection and segmentation tasks. Generalizability of the best suited model was then tested on the datasets constructed

Abstract

A method for real-time detection of nerves together with a needle and its trajectory in ultrasound videos to autonomously guide the needle towards nerve block target. We also publicly release needle annotations for further research.

Abstract

Biological processes like growth, aging, and disease progression are generally studied with follow-up scans taken at different time points, i.e., with image time series (TS) based analysis. Comparison between TS representing a biological process of two individuals/populations is of interest. A metric to quantify the difference between TS is desirable for such a comparison. The two TS represent the evolution of two different subject/population average anatomies through two paths. A method to untangle and quantify the path and inter-subject anatomy(shape) difference between the TS is presented in this paper. The proposed metric is a generalized version of Fréchet distance designed to compare curves. The proposed method is evaluated with simulated and adult and fetal neuro templates. Results show that the metric is able to separate and quantify the path and shape differences between TS. Keywords: Image TS · Time-dependant variation · Time dependant variation.

Abstract

Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) are neurogenerative impairments with similar symptoms and risk factors. Sulcal width and depth are known biomarkers for discriminating between AD and MCI. This paper presents a novel 2D image representation for a brain mesh surface, called a height map. The basic idea behind the height map is to represent the surface as a function of spherical coordinates of the mesh vertices. We present a method to derive a height map from a given neuroimage (MRI) and extract sulcal regions from the height map. We demonstrate the height map’s utility for classifying a given neuroimage into healthy, MCI and AD classes. Two approaches for extracting sulcal regions are explored. The proposed method is computationally light, and obtaining sulcal regions from a brain surface mesh takes about 24 seconds on a standard Intel i5-7200 CPU. The proposed method achieves 76.1% accuracy, and 76.3% F1-score for healthy, MCI, AD classification on a publicly available dataset.

Abstract

Normative aging trends of the brain can serve as an important reference in the assessment of neurological structural disorders. Such models are typically developed from longitudinal brain image data—follow-up data of the same subject over diferent time points. In practice, obtaining such longitudinal data is difcult. We propose a method to develop an aging model for a given population, in the absence of longitudinal data, by using images from diferent subjects at diferent time points, the so-called cross-sectional data. We defne an aging model as a difeomorphic deformation on a structural template derived from the data and propose a method that develops topology preserving aging model close to natural aging. The proposed model is successfully validated on two public crosssectional datasets which provide templates constructed from diferent sets of subjects at diferent age points.