# Models to Identify Small Brain White Matter Hyperintensity Lesions
## 5 Discussion  
This work introduced a deep learning-based approach for detecting, segmenting, and classifying White Matter Hyperintensities (WMHs) related to ischemia and demyelination diseases. According to the literature, most works primarily focus on the segmentation process [34, 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126], including a MICCAI challenge finalized in 2023 [75, 121]. However, [PERSON] et al. [38] noted that most methods are unsuitable for segmenting small lesion sizes with complex boundary shapes and diffuse edges, as in the case of demyelination lesions or ischemia lesions in the first stages. Our approach incorporated detection and classification, providing a comprehensive framework for lesion analysis. Regarding the segmentation task explored and proved in this work, we can say that the proposed UNet model has a good performance on the segmentation, e.g., we can see in Table 11 that DSC values of different works [34, 38, 100, 101, 102, 103] oscillate between 0.67 and 0.95, which means that the UNet network demonstrates its ability to extract the features map of the lesions and in that way get a good prediction of the lesions. In our case, to improve the segmentation and reduce the possible sources of noise that can affect the model, we only  
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selected the range of slices of each volume that have the lesions with their corresponding annotations. We achieved accuracy with a mean value of 0.95 for the Dice coefficient (see Table 10). Nevertheless, we have to say that the model has difficulty segmenting the small lesions. Several studies proposed the uses of the 3D UNet and their variations, like that of [PERSON] et al. [117], who used a 3D UNet with such segmenting strokes, and their results have an IoU score of 0.88. Along the same lines, [PERSON] et al. [126] proposed the uses of 3D Unet for the segmentation of tumors and WMHs, and they reported results of a mean Dice value for the WMH segmentation of 0.62 and the tumor of 0.92. This project did not use 3D Unet because the diseases are not continuous in the slices, and it is difficult to construct a volume of the lesions without including false information. Concerning the SAM model, and based on our knowledge of the literature search at the moment, there are no studies related to the segmentation of WMHs using the SAM model. However, we can find several works related to the detection of brain tumors [127], breast lesions of ultrasound images [47; 52], and retinal fundus images [48]. In order to compare our results, we selected the paper from [PERSON] et al. [127] related to brain tumors. In this sense, the SAM model was tested automatically in this project, and our results agree with several works related to segmentations of brain lesions. Our project's Dice mean values stay in the range of 0.3 to 0.85, which is in agreement with Dice values of 0.53 to 0.88 in the segmentation of the tumors, according to the reported results from [127; 128]. In our project, the highest dice values detected are when the lesions are bigger and relatively homogeneously distributed in the image (see Figure 15). Also, some recent studies, such as [46], have suggested that SAM has more of a possibility of success for the segmentation of medical images in the prompt mode because, in this case, the user gives the model the region of interest for the segmentation. Here, our proposed method could be easily adapted for that user who gives the prompt of the brain lesion. In this sense, that allows us to improve the annotations and the acquisition of better masks for posterior processing. Regarding the segmentation, detection, and classification of YOLO and Detectron2, we can say that the results from the YOLOv8 model show that the model is better at detecting big lesions than small ones but also has difficulties with classification.