TY  - BOOK
AB  - Medical image registration methods can strongly benefit from anatomical labels, which can be provided by segmentation networks at reduced labeling effort. Yet, label noise may adversely affect registration performance. In this work, we propose a quality-aware segmentation-guided registration method that handles such noisy, i.e., low-quality, labels by self-correcting them using Confident Learning. Utilizing NLST and in-house acquired abdominal MR images, we show that our proposed quality-aware method effectively addresses the drop in registration performance observed in quality-unaware methods. Our findings demonstrate that incorporating an appropriate label-correction strategy during training can reduce labeling efforts, consequently enhancing the practicality of segmentation-guided registration.
AU  - Raveendran, V.*
AU  - Spieker, V.
AU  - Braren, R.F.*
AU  - Karampinos, D.C.*
AU  - Zimmer, V.A.*
AU  - Schnabel, J.A.*
C1  - 70409
C2  - 55399
SP  - 33-38
TI  - Segmentation-guided Medical Image Registration: Quality Awareness using Label Noise Correctionn.
JO  - Inf. aktuell
PY  - 2024
SN  - 1431-472X
ER  - 

TY  - CONF
AB  - The segment anything model (SAM) has recently emerged as a significant breakthrough in foundation models, demonstrating remarkable zero-shot performance in object segmentation tasks. While SAM is designed for generalization, it exhibits limitations in handling specific medical imaging tasks that require fine-structure segmentation or precise boundaries. In this paper, we focus on the task of cardiac magnetic resonance imaging (cMRI) short-axis view segmentation using the SAM foundation model. We conduct a comprehensive investigation of the impact of different prompting strategies (including bounding boxes, positive points, negative points, and their combinations) on segmentation performance. We evaluate on two public datasets using the baseline model and models fine-tuned with varying amounts of annotated data, ranging from a limited number of volumes to a fully annotated dataset. Our findings indicate that prompting strategies significantly influence segmentation performance. Combining positive points with either bounding boxes or negative points shows substantial benefits, but little to no benefit when combined simultaneously. We further observe that fine-tuning SAM with a few annotated volumes improves segmentation performance when properly prompted. Specifically, fine-tuning with bounding boxes has a positive impact, while fine-tuning without bounding boxes leads to worse results compared to baseline.
AU  - Stein, J.
AU  - di Folco, M.
AU  - Schnabel, J.A.
C1  - 71697
C2  - 56136
CY  - Abraham-lincoln-str. 46, Wiesbaden, 65189, Germany
SP  - 54-59
TI  - Influence of prompting strategies on segment anything model (SAM) for short-axis cardiac MRI segmentation.
JO  - Inf. aktuell
PB  - Springer Vieweg Verlag
PY  - 2024
SN  - 1431-472X
ER  - 

TY  - CONF
AB  - Fast and accurate morphological classification of cells in bone marrow samples is a key step in the diagnostic workup of many disorders of the hematopoietic system such as leukemias. In spite of its long-established key position, morphological examination of bone marrow samples has been difficult to automatise, and is still mainly performed manually by trained cytologists on light microscopes. In our contribution [1], we present a neural network for classification of light microscopy images of bone marrow samples.
AU  - Matek, C.
AU  - Krappe, S.*
AU  - Münzenmayer, C.*
AU  - Haferlach, T.*
AU  - Marr, C.
C1  - 67190
C2  - 53465
SP  - 159
TI  - Abstract: A database and neural network for highly accurate classification of single bone marrow cells.
JO  - Inf. aktuell
VL  - 923
PY  - 2022
SN  - 1431-472X
ER  - 

TY  - BOOK
AB  - Reliable recognition and microscopic differentiation of malignant and non-malignant leukocytes from peripheral blood smears is a key task of cytological diagnostics in hematology [1]. Having been practised for well over a century, cytomorphological analysis is still today routinely performed by human examiners using optical microscopes, a process that can be tedious, time-consuming, and suffering from considerable intra-and inter-rater variability [2]. Our work aims to provide a more quantitative and robust decision-aid for the differentiation of single blood cells in general and recognition of blast cells characteristic for Acute Myeloid Leukemia (AML) in particular.
AU  - Matek, C.
AU  - Schwarz, S.*
AU  - Spiekermann, K.*
AU  - Marr, C.
C1  - 58864
C2  - 48633
SP  - 53-54
TI  - Abstract: Recognition of AML blast cells in a curated single-cell dataset of leukocyte morphologies using deep convolutional neural networks.
JO  - Inf. aktuell
PY  - 2020
SN  - 1431-472X
ER  -