Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images.

Purpose: Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3D search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors.

The transverse occipital sulcus and intraparietal sulcus show neural selectivity to object-scene size relationships.

To optimize visual search, humans attend to objects with the expected size of the sought target relative to its surrounding scene (object-scene scale consistency). We investigate how the human brain responds to variations in object-scene scale consistency. We use functional magnetic resonance imaging and a voxel-wise feature encoding model to estimate tuning to different object/scene properties.

Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets.

Advances in 3D imaging technology are transforming how radiologists search for cancer and how security officers scrutinize baggage for dangerous objects. These new 3D technologies often improve search over 2D images but vastly increase the image data. Here, we investigate 3D search for targets of various sizes in filtered noise and digital breast phantoms.

Evaluation of Convolutional Neural Networks for Search in 1/f Filtered Noise and Digital Breast Tomosynthesis Phantoms.

With the advent of powerful convolutional neural networks (CNNs), recent studies have extended early applications of neural networks to imaging tasks thus making CNNs a potential new tool for assessing medical image quality. Here, we compare a CNN to model observers in a search task for two possible signals (a simulated mass and a smaller simulated micro-calcification) embedded in filtered noise and single slices of Digital Breast Tomosynthesis (DBT) virtual phantoms. For the case of the filtered noise, we show how a CNN can approximate the ideal observer for a search task, achieving a statistical efficiency of 0.