Accurate segmentation of anatomical structures and abnormalities in medical images is crucial, but manual segmentation is time-consuming and automated approaches lack clinical accuracy. In recent years, active learning approaches that aim to combine automatic segmentation with manual input have gained attention in the field, aiming to reduce the annotation effort required for training segmentation models. Batch query retrieval is a key component of active learning as it is a technique that allows for the simultaneous selection of multiple regions/points for annotation. This study investigates the effectiveness of discrete batch query retrieval methods compared to the traditional approach using gradient descent in the context of 3D medical image segmentation. Our experiments show that the active learning paradigm with batch query retrieval provides a few advantages over the gradient descent approach. Additionally, we analyze the impact of discretizing the query retrieval strategy on system performance. Our findings suggest that discretization can lead to slight performance degradation in terms of segmentation quality but offer computational advantages and faster convergence. We also discuss open issues, such as the interpretability of active learning methods, and recommend further research on combining active learning with other segmentation techniques. Overall, our study contributes to the understanding of active learning in medical image segmentation and provides insights for developing more efficient and accurate interactive segmentation models.

Segmentation of 3D medical images is useful for various medical tasks. However, fully automated segmentation lacks the accuracy required for medical purposes while manual segmentation is too time-consuming. Therefore, an active learning method can be used to generate an accurate segmentation using less user input. The active learning pipeline consists of automatic 3D segmentation, generation of a 3D uncertainty field and an optimization algorithm finding the optimal plane in the uncertainty field. This plane can then be shown to a user to be labeled so that the segmentation can be improved. This process is repeated until the user is satisfied with the output. If the plane is chosen so that it contains more errors, then less user input will be needed. This paper focusses on evaluating different optimization algorithms in the context of this pipeline. The three algorithms that are evaluated are particle swarm optimization, gradient descent and L-BFGS-B. The results of the evaluation show that particle swarm optimization converges the quickest, but to lower values than gradient descent. Gradient descent converges slowly, but to high values. L-BFGS-B converges quickly to values that are as high as those from gradient descent. Therefore, using L-BFGS-B in the pipeline instead of gradient descent will decrease the runtime of the pipeline. Using particle swarm optimization will decrease the runtime even further, but at the cost of requiring more user input to obtain a segmentation of acceptable quality.

Although automated segmentation of 3D medical images produce near-ideal results, they encounter limitations and occasional errors, necessitating manual intervention for error correction. Recent studies introduce an active learning pipeline as an efficient solution for this, requiring user corrections only on some of the most uncertain parts of
the automatically segmented image. It does so by combining different uncertainty fields, which are various ways to quantify possible errors. We investigate into its individual uncertainty fields and their combination scheme in attempt to validate its methods. Additionally, we replace its methods for estimating uncertainty with another common way to do so, called the ensemble method, to test possible improvements at uncertainty estimation. Results of this research validates the combination method of the active learning pipeline, and shows weak advantages but strong disadvantages of the ensemble method when compared to the combined method of the active learning pipeline.