7 research outputs found
A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation
Case-Based Reasoning models are one of the most used reasoning paradigms in expert-knowledge-driven areas. One of the most prominent fields of use of these systems is the medical sector, where explainable models are required. However, these models are considerably reliant on user input and the introduction of relevant curated data. Deep learning approaches offer an analogous solution, where user input is not required. This paper proposes a hybrid Case-Based Reasoning, Deep Learning framework for medical-related applications, focusing on the generation of medical reports. The proposal combines the explainability and user-focused approach of case-based reasoning models with the deep learning techniques performance. Moreover, the framework is fully modular to fit a wide variety of tasks and data, such as real-time sensor captured data, images, or text, to name a few. An implementation of the proposed framework focusing on radiology report generation assistance is provided. This implementation is used to evaluate the proposal, showing that it can provide meaningful and accurate corrections, even when the amount of information available is minimal. Additional tests on the optimization degree of the case base are also performed, evidencing how the proposed framework can optimize this base to achieve optimal performance
Case series of breast fillers and how things may go wrong: radiology point of view
INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging
breast due to breastfeeding or aging as well as small breast size. Recent years have shown the
emergence of a variety of injectable materials on market as breast fillers. These injectable
breast fillers have swiftly gained popularity among women, considering the minimal
invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know
that the procedure may pose detrimental complications, while visualization of breast
parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic
challenges. We present a case series of three patients with prior history of hyaluronic acid and
collagen breast injections.
REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening
shortness of breath, non-productive cough, central chest pain; associated with fever and chills
for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever
and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases
revealed non thrombotic wedge-shaped peripheral air-space densities.
The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2-
weeks duration. Previous collagen breast injection performed 1 year ago had impeded
sonographic visualization of the breast parenchyma. MRI breasts showed multiple non-
enhancing round and oval shaped lesions exhibiting fat intensity.
CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well
as limitations of imaging posed by breast fillers such that MRI is required as problem-solving
tool
Characterization of alar ligament on 3.0T MRI: a cross-sectional study in IIUM Medical Centre, Kuantan
INTRODUCTION: The main purpose of the study is to compare the normal anatomy of alar
ligament on MRI between male and female. The specific objectives are to assess the prevalence
of alar ligament visualized on MRI, to describe its characteristics in term of its course, shape and
signal homogeneity and to find differences in alar ligament signal intensity between male and
female. This study also aims to determine the association between the heights of respondents
with alar ligament signal intensity and dimensions.
MATERIALS & METHODS: 50 healthy volunteers were studied on 3.0T MR scanner
Siemens Magnetom Spectra using 2-mm proton density, T2 and fat-suppression sequences. Alar
ligament is depicted in 3 planes and the visualization and variability of the ligament courses,
shapes and signal intensity characteristics were determined. The alar ligament dimensions were
also measured.
RESULTS: Alar ligament was best depicted in coronal plane, followed by sagittal and axial
planes. The orientations were laterally ascending in most of the subjects (60%), predominantly
oval in shaped (54%) and 67% showed inhomogenous signal. No significant difference of alar
ligament signal intensity between male and female respondents. No significant association was
found between the heights of the respondents with alar ligament signal intensity and dimensions.
CONCLUSION: Employing a 3.0T MR scanner, the alar ligament is best portrayed on coronal
plane, followed by sagittal and axial planes. However, tremendous variability of alar ligament as
depicted in our data shows that caution needs to be exercised when evaluating alar ligament,
especially during circumstances of injury
Segmentation of deformed kidneys and nephroblastoma using Case-Based Reasoning and Convolutional Neural Network
International audienceMost often, image segmentation is not fully automated and a user is required to lead the process in order to obtain correct results. In a medical context, segmentation can furnish much information to surgeons, but this task is rarely executed. Artificial Intelligence (AI) is a powerful approach for devising a viable solution to fully automated treatment. In this paper, we have focused on kidneys deformed by nephroblastoma. However, a frequent medical constraint is encountered which is a lack of sufficient data with which to train our system. In function of this constraint, two AI approaches were used to segment these structures. First, a Case Based Reasoning (CBR) approach was defined which can enhance the growth of regions for segmentation of deformed kidneys using an adaptation phase to modify coordinates of recovered seeds. This CBR approach was confronted with manual region growing and a Convolutional Neural Network (CNN). The CBR system succeeded in performing the best segmentation for the kidney with a mean Dice of 0.83. Deep Learning was then examined as a possible solution, using the latest performing networks for image segmentation. However, for relevant efficiency, this method requires a large data set. An option would be to manually segment only certain representative slices from a patient and then use them to train a Convolutional Neural Network (CNN) how to segment. In this article the authors propose an evaluation of a CNN for medical image segmentation following different training sets with a variable number of manual segmentations. To choose slices to train the CNN, an Overlearning Vector for Valid Sparse SegmentatIONs (OV ASSION) was used, with the notion of gap between two slices from the training set. This protocol made it possible to obtain reliable segmentations of per patient with a small data set and to determine that only 26% of initial segmented slices are required to obtain a complete segmentation of a patient with a mean Dice of 0.897
Segmentation of deformed kidneys and nephroblastoma using Case-Based Reasoning and Convolutional Neural Network
International audienceMost often, image segmentation is not fully automated and a user is required to lead the process in order to obtain correct results. In a medical context, segmentation can furnish much information to surgeons, but this task is rarely executed. Artificial Intelligence (AI) is a powerful approach for devising a viable solution to fully automated treatment. In this paper, we have focused on kidneys deformed by nephroblastoma. However, a frequent medical constraint is encountered which is a lack of sufficient data with which to train our system. In function of this constraint, two AI approaches were used to segment these structures. First, a Case Based Reasoning (CBR) approach was defined which can enhance the growth of regions for segmentation of deformed kidneys using an adaptation phase to modify coordinates of recovered seeds. This CBR approach was confronted with manual region growing and a Convolutional Neural Network (CNN). The CBR system succeeded in performing the best segmentation for the kidney with a mean Dice of 0.83. Deep Learning was then examined as a possible solution, using the latest performing networks for image segmentation. However, for relevant efficiency, this method requires a large data set. An option would be to manually segment only certain representative slices from a patient and then use them to train a Convolutional Neural Network (CNN) how to segment. In this article the authors propose an evaluation of a CNN for medical image segmentation following different training sets with a variable number of manual segmentations. To choose slices to train the CNN, an Overlearning Vector for Valid Sparse SegmentatIONs (OV ASSION) was used, with the notion of gap between two slices from the training set. This protocol made it possible to obtain reliable segmentations of per patient with a small data set and to determine that only 26% of initial segmented slices are required to obtain a complete segmentation of a patient with a mean Dice of 0.897